{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Tutorial 2: Training a spiking neural network on a simple vision dataset\n",
    "\n",
    "Friedemann Zenke (https://fzenke.net)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> For more details on surrogate gradient learning, please see: \n",
    "> Neftci, E.O., Mostafa, H., and Zenke, F. (2019). Surrogate Gradient Learning in Spiking Neural Networks.\n",
    "> https://arxiv.org/abs/1901.09948"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In Tutorial 1, we have seen how to train a simple multi-layer spiking neural network on a small synthetic dataset. In this tutorial, we will apply what we have learned so far to a slightly larger dataset.\n",
    "Concretely, we will use the [Fashion MNIST dataset](https://github.com/zalandoresearch/fashion-mnist). "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.gridspec import GridSpec\n",
    "import seaborn as sns\n",
    "\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import torchvision"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'1.7.0'"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "torch.__version__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# The coarse network structure is dicated by the Fashion MNIST dataset. \n",
    "nb_inputs  = 28*28\n",
    "nb_hidden  = 100\n",
    "nb_outputs = 10\n",
    "\n",
    "time_step = 1e-3\n",
    "nb_steps  = 100\n",
    "\n",
    "batch_size = 256"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "dtype = torch.float\n",
    "\n",
    "# Check whether a GPU is available\n",
    "if torch.cuda.is_available():\n",
    "    device = torch.device(\"cuda\")     \n",
    "else:\n",
    "    device = torch.device(\"cpu\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Here we load the Dataset\n",
    "root = os.path.expanduser(\"~/data/datasets/torch/fashion-mnist\")\n",
    "train_dataset = torchvision.datasets.FashionMNIST(root, train=True, transform=None, target_transform=None, download=True)\n",
    "test_dataset = torchvision.datasets.FashionMNIST(root, train=False, transform=None, target_transform=None, download=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Standardize data\n",
    "# x_train = torch.tensor(train_dataset.train_data, device=device, dtype=dtype)\n",
    "x_train = np.array(train_dataset.data, dtype=np.float)\n",
    "x_train = x_train.reshape(x_train.shape[0],-1)/255\n",
    "# x_test = torch.tensor(test_dataset.test_data, device=device, dtype=dtype)\n",
    "x_test = np.array(test_dataset.data, dtype=np.float)\n",
    "x_test = x_test.reshape(x_test.shape[0],-1)/255\n",
    "\n",
    "# y_train = torch.tensor(train_dataset.train_labels, device=device, dtype=dtype)\n",
    "# y_test  = torch.tensor(test_dataset.test_labels, device=device, dtype=dtype)\n",
    "y_train = np.array(train_dataset.targets, dtype=np.int)\n",
    "y_test  = np.array(test_dataset.targets, dtype=np.int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-0.5, 27.5, 27.5, -0.5)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Here we plot one of the raw data points as an example\n",
    "data_id = 1\n",
    "plt.imshow(x_train[data_id].reshape(28,28), cmap=plt.cm.gray_r)\n",
    "plt.axis(\"off\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since we are working with spiking neural networks, we ideally want to use a temporal code to make use of spike timing. To that end, we will use a spike latency code to feed spikes to our network."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "def current2firing_time(x, tau=20, thr=0.2, tmax=1.0, epsilon=1e-7):\n",
    "    \"\"\" Computes first firing time latency for a current input x assuming the charge time of a current based LIF neuron.\n",
    "\n",
    "    Args:\n",
    "    x -- The \"current\" values\n",
    "\n",
    "    Keyword args:\n",
    "    tau -- The membrane time constant of the LIF neuron to be charged\n",
    "    thr -- The firing threshold value \n",
    "    tmax -- The maximum time returned \n",
    "    epsilon -- A generic (small) epsilon > 0\n",
    "\n",
    "    Returns:\n",
    "    Time to first spike for each \"current\" x\n",
    "    \"\"\"\n",
    "    idx = x<thr\n",
    "    x = np.clip(x,thr+epsilon,1e9)\n",
    "    T = tau*np.log(x/(x-thr))\n",
    "    T[idx] = tmax\n",
    "    return T\n",
    " \n",
    "\n",
    "def sparse_data_generator(X, y, batch_size, nb_steps, nb_units, shuffle=True ):\n",
    "    \"\"\" This generator takes datasets in analog format and generates spiking network input as sparse tensors. \n",
    "\n",
    "    Args:\n",
    "        X: The data ( sample x event x 2 ) the last dim holds (time,neuron) tuples\n",
    "        y: The labels\n",
    "    \"\"\"\n",
    "\n",
    "    labels_ = np.array(y,dtype=np.int)\n",
    "    number_of_batches = len(X)//batch_size\n",
    "    sample_index = np.arange(len(X))\n",
    "\n",
    "    # compute discrete firing times\n",
    "    tau_eff = 20e-3/time_step\n",
    "    firing_times = np.array(current2firing_time(X, tau=tau_eff, tmax=nb_steps), dtype=np.int)\n",
    "    unit_numbers = np.arange(nb_units)\n",
    "\n",
    "    if shuffle:\n",
    "        np.random.shuffle(sample_index)\n",
    "\n",
    "    total_batch_count = 0\n",
    "    counter = 0\n",
    "    while counter<number_of_batches:\n",
    "        batch_index = sample_index[batch_size*counter:batch_size*(counter+1)]\n",
    "\n",
    "        coo = [ [] for i in range(3) ]\n",
    "        for bc,idx in enumerate(batch_index):\n",
    "            c = firing_times[idx]<nb_steps\n",
    "            times, units = firing_times[idx][c], unit_numbers[c]\n",
    "\n",
    "            batch = [bc for _ in range(len(times))]\n",
    "            coo[0].extend(batch)\n",
    "            coo[1].extend(times)\n",
    "            coo[2].extend(units)\n",
    "\n",
    "        i = torch.LongTensor(coo).to(device)\n",
    "        v = torch.FloatTensor(np.ones(len(coo[0]))).to(device)\n",
    "    \n",
    "        X_batch = torch.sparse.FloatTensor(i, v, torch.Size([batch_size,nb_steps,nb_units])).to(device)\n",
    "        y_batch = torch.tensor(labels_[batch_index],device=device)\n",
    "\n",
    "        yield X_batch.to(device=device), y_batch.to(device=device)\n",
    "\n",
    "        counter += 1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Setup of the spiking network model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "tau_mem = 10e-3\n",
    "tau_syn = 5e-3\n",
    "\n",
    "alpha   = float(np.exp(-time_step/tau_syn))\n",
    "beta    = float(np.exp(-time_step/tau_mem))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "init done\n"
     ]
    }
   ],
   "source": [
    "weight_scale = 7*(1.0-beta) # this should give us some spikes to begin with\n",
    "\n",
    "w1 = torch.empty((nb_inputs, nb_hidden),  device=device, dtype=dtype, requires_grad=True)\n",
    "torch.nn.init.normal_(w1, mean=0.0, std=weight_scale/np.sqrt(nb_inputs))\n",
    "\n",
    "w2 = torch.empty((nb_hidden, nb_outputs), device=device, dtype=dtype, requires_grad=True)\n",
    "torch.nn.init.normal_(w2, mean=0.0, std=weight_scale/np.sqrt(nb_hidden))\n",
    "\n",
    "print(\"init done\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_voltage_traces(mem, spk=None, dim=(3,5), spike_height=5):\n",
    "    gs=GridSpec(*dim)\n",
    "    if spk is not None:\n",
    "        dat = 1.0*mem\n",
    "        dat[spk>0.0] = spike_height\n",
    "        dat = dat.detach().cpu().numpy()\n",
    "    else:\n",
    "        dat = mem.detach().cpu().numpy()\n",
    "    for i in range(np.prod(dim)):\n",
    "        if i==0: a0=ax=plt.subplot(gs[i])\n",
    "        else: ax=plt.subplot(gs[i],sharey=a0)\n",
    "        ax.plot(dat[i])\n",
    "        ax.axis(\"off\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can now run this code and plot the output layer \"membrane potentials\" below. As desired, these potentials do not have spikes riding on them."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Training the network"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "class SurrGradSpike(torch.autograd.Function):\n",
    "    \"\"\"\n",
    "    Here we implement our spiking nonlinearity which also implements \n",
    "    the surrogate gradient. By subclassing torch.autograd.Function, \n",
    "    we will be able to use all of PyTorch's autograd functionality.\n",
    "    Here we use the normalized negative part of a fast sigmoid \n",
    "    as this was done in Zenke & Ganguli (2018).\n",
    "    \"\"\"\n",
    "    \n",
    "    scale = 100.0 # controls steepness of surrogate gradient\n",
    "\n",
    "    @staticmethod\n",
    "    def forward(ctx, input):\n",
    "        \"\"\"\n",
    "        In the forward pass we compute a step function of the input Tensor\n",
    "        and return it. ctx is a context object that we use to stash information which \n",
    "        we need to later backpropagate our error signals. To achieve this we use the \n",
    "        ctx.save_for_backward method.\n",
    "        \"\"\"\n",
    "        ctx.save_for_backward(input)\n",
    "        out = torch.zeros_like(input)\n",
    "        out[input > 0] = 1.0\n",
    "        return out\n",
    "\n",
    "    @staticmethod\n",
    "    def backward(ctx, grad_output):\n",
    "        \"\"\"\n",
    "        In the backward pass we receive a Tensor we need to compute the \n",
    "        surrogate gradient of the loss with respect to the input. \n",
    "        Here we use the normalized negative part of a fast sigmoid \n",
    "        as this was done in Zenke & Ganguli (2018).\n",
    "        \"\"\"\n",
    "        input, = ctx.saved_tensors\n",
    "        grad_input = grad_output.clone()\n",
    "        grad = grad_input/(SurrGradSpike.scale*torch.abs(input)+1.0)**2\n",
    "        return grad\n",
    "    \n",
    "# here we overwrite our naive spike function by the \"SurrGradSpike\" nonlinearity which implements a surrogate gradient\n",
    "spike_fn  = SurrGradSpike.apply"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "def run_snn(inputs):\n",
    "    h1 = torch.einsum(\"abc,cd->abd\", (inputs, w1))\n",
    "    syn = torch.zeros((batch_size,nb_hidden), device=device, dtype=dtype)\n",
    "    mem = torch.zeros((batch_size,nb_hidden), device=device, dtype=dtype)\n",
    "\n",
    "    mem_rec = []\n",
    "    spk_rec = []\n",
    "\n",
    "    # Compute hidden layer activity\n",
    "    for t in range(nb_steps):\n",
    "        mthr = mem-1.0\n",
    "        out = spike_fn(mthr)\n",
    "        rst = out.detach() # We do not want to backprop through the reset\n",
    "\n",
    "        new_syn = alpha*syn +h1[:,t]\n",
    "        new_mem = (beta*mem +syn)*(1.0-rst)\n",
    "\n",
    "        mem_rec.append(mem)\n",
    "        spk_rec.append(out)\n",
    "        \n",
    "        mem = new_mem\n",
    "        syn = new_syn\n",
    "\n",
    "    mem_rec = torch.stack(mem_rec,dim=1)\n",
    "    spk_rec = torch.stack(spk_rec,dim=1)\n",
    "\n",
    "    # Readout layer\n",
    "    h2= torch.einsum(\"abc,cd->abd\", (spk_rec, w2))\n",
    "    flt = torch.zeros((batch_size,nb_outputs), device=device, dtype=dtype)\n",
    "    out = torch.zeros((batch_size,nb_outputs), device=device, dtype=dtype)\n",
    "    out_rec = [out]\n",
    "    for t in range(nb_steps):\n",
    "        new_flt = alpha*flt +h2[:,t]\n",
    "        new_out = beta*out +flt\n",
    "\n",
    "        flt = new_flt\n",
    "        out = new_out\n",
    "\n",
    "        out_rec.append(out)\n",
    "\n",
    "    out_rec = torch.stack(out_rec,dim=1)\n",
    "    other_recs = [mem_rec, spk_rec]\n",
    "    return out_rec, other_recs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "def train(x_data, y_data, lr=2e-3, nb_epochs=10):\n",
    "    params = [w1,w2]\n",
    "    optimizer = torch.optim.Adam(params, lr=lr, betas=(0.9,0.999))\n",
    "\n",
    "    log_softmax_fn = nn.LogSoftmax(dim=1)\n",
    "    loss_fn = nn.NLLLoss()\n",
    "    \n",
    "    loss_hist = []\n",
    "    for e in range(nb_epochs):\n",
    "        local_loss = []\n",
    "        for x_local, y_local in sparse_data_generator(x_data, y_data, batch_size, nb_steps, nb_inputs):\n",
    "            output,_ = run_snn(x_local.to_dense())\n",
    "            m,_=torch.max(output,1)\n",
    "            log_p_y = log_softmax_fn(m)\n",
    "            loss_val = loss_fn(log_p_y, y_local)\n",
    "\n",
    "            optimizer.zero_grad()\n",
    "            loss_val.backward()\n",
    "            optimizer.step()\n",
    "            local_loss.append(loss_val.item())\n",
    "        mean_loss = np.mean(local_loss)\n",
    "        print(\"Epoch %i: loss=%.5f\"%(e+1,mean_loss))\n",
    "        loss_hist.append(mean_loss)\n",
    "        \n",
    "    return loss_hist\n",
    "        \n",
    "        \n",
    "def compute_classification_accuracy(x_data, y_data):\n",
    "    \"\"\" Computes classification accuracy on supplied data in batches. \"\"\"\n",
    "    accs = []\n",
    "    for x_local, y_local in sparse_data_generator(x_data, y_data, batch_size, nb_steps, nb_inputs, shuffle=False):\n",
    "        output,_ = run_snn(x_local.to_dense())\n",
    "        m,_= torch.max(output,1) # max over time\n",
    "        _,am=torch.max(m,1)      # argmax over output units\n",
    "        tmp = np.mean((y_local==am).detach().cpu().numpy()) # compare to labels\n",
    "        accs.append(tmp)\n",
    "    return np.mean(accs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1: loss=0.89463\n",
      "Epoch 2: loss=0.53745\n",
      "Epoch 3: loss=0.48071\n",
      "Epoch 4: loss=0.44689\n",
      "Epoch 5: loss=0.42401\n",
      "Epoch 6: loss=0.40686\n",
      "Epoch 7: loss=0.39227\n",
      "Epoch 8: loss=0.37885\n",
      "Epoch 9: loss=0.36825\n",
      "Epoch 10: loss=0.36175\n",
      "Epoch 11: loss=0.35250\n",
      "Epoch 12: loss=0.34323\n",
      "Epoch 13: loss=0.33708\n",
      "Epoch 14: loss=0.33294\n",
      "Epoch 15: loss=0.32420\n",
      "Epoch 16: loss=0.31920\n",
      "Epoch 17: loss=0.31117\n",
      "Epoch 18: loss=0.30753\n",
      "Epoch 19: loss=0.30294\n",
      "Epoch 20: loss=0.29746\n",
      "Epoch 21: loss=0.29385\n",
      "Epoch 22: loss=0.28760\n",
      "Epoch 23: loss=0.28413\n",
      "Epoch 24: loss=0.27772\n",
      "Epoch 25: loss=0.27499\n",
      "Epoch 26: loss=0.27133\n",
      "Epoch 27: loss=0.26617\n",
      "Epoch 28: loss=0.26376\n",
      "Epoch 29: loss=0.26041\n",
      "Epoch 30: loss=0.25375\n"
     ]
    }
   ],
   "source": [
    "loss_hist = train(x_train, y_train, lr=2e-4, nb_epochs=30)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 495x300 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(3.3,2),dpi=150)\n",
    "plt.plot(loss_hist)\n",
    "plt.xlabel(\"Epoch\")\n",
    "plt.ylabel(\"Loss\")\n",
    "sns.despine()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training accuracy: 0.912\n",
      "Test accuracy: 0.863\n"
     ]
    }
   ],
   "source": [
    "print(\"Training accuracy: %.3f\"%(compute_classification_accuracy(x_train,y_train)))\n",
    "print(\"Test accuracy: %.3f\"%(compute_classification_accuracy(x_test,y_test)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_mini_batch(x_data, y_data, shuffle=False):\n",
    "    for ret in sparse_data_generator(x_data, y_data, batch_size, nb_steps, nb_inputs, shuffle=shuffle):\n",
    "        return ret "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_batch, y_batch = get_mini_batch(x_test, y_test)\n",
    "output, other_recordings = run_snn(x_batch.to_dense())\n",
    "mem_rec, spk_rec = other_recordings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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nTKCXsClyM0jgILXERRlFU6K0zC/h9tggK7fZf7zxYVPYPoO2yys4OK384p/99B+zfY42aA1kN8/qcn61eF/3w3N37Gyft6PRa6QegtT0mJ8cuhztineGyvCvYtExE4Zk7boxv3WUDx+ZkWekYf5ZGItLAa7WHLsr0tfDr958dcLMpJ9MBny3xsOBh266c41suflpH1AGJOpWr0ye5Es85ZFCG1npr4wBVhjbWK+POn9MzUEZAndyBLfMjyrKPy96U4K6vc1coG2njxgb5Cws/BiYes5uaXk2Wsk2/9h5s6NQ+hpd1oQwbqJckU2gwgXT2YX6yYVG/8IOxBkIxJkqdeZaBlFjOcIY1ZMUiigQcSGjFMn+NJP6F4gq5epdfq2oXQ+f2xcYOFcGW74++9ufzhrhXRv1YNuvgf9rWNUwOE7X8fpDO3yid1OzOUtrHvnsx+bt2lN82fwlMVGgpdoOlP5X/iWxHEe1g8gKzbRRwsHVDKRbi+0sADVPm5qrLZ+aq70EuOSqgQt+8Oev/aj9oL/jiT5j8CEV2//kRdM3m+RbsDlgPjBn6HNaKaH377mkOtGytNJOVupONtaP0ttc4Tz11wXf29UV3Q+QABKvN8+IEpQBbGTBIfsl3VqAAU3wd/oDxJTFj3gf3ZSDBgEFVvIKLklCtCwu7nhPhLVVszg4aRrpYEwjH2dRAVz+nMpcXp9rpSLRStl//Et2WUPr5u7yOtleVh60TKPPn8vsLe/vei6QjD9y889+1fLa58LE4iJiWSOET45awUlh8r2SIn5aPDpSp3pwGqmeG8f8dsCtZnP6emZZs5kmFVXEiAlBRsbIqgiucOma4kQNEXzTJN180zJDY89nHlV+zUAITQDO/pufeqoD9dhu3Md/Rm7tE6uvOfYO1NMEKbQwQBV93On7Gn31QEFfvjQ1rBUVqMjYEBh3ZoxrfNv5ctOPEEBSBdjuXsyjYin/Wz6jPDG0kEUlgq4hT+yVmH9/8DOPXSd082sJV62Pu/JLaXPwjZaWK0prOTEg0rkBYXUnHfF8eLD+t3438sInf3xp92uTE68eoQ5f1/W4mX7zvRd+IL3xGVl6eL+BbxD0gf044QB2SQUMu5KkjiN1/FYxkWwVRtaPcjuQ9kGkvRslh3XWROjlaHo5Qi9D+gwSAUFaDzOoh3F0G+Xrci3/3t6B6O5n90e7/+To8hGgc7iCX3jHQh8wmXwLK79yZP4VAcK6UukZlj2wIpsdvC6Z7l/whe5j7wCZYG79/k1y7oK/HObCrm/JMGtf6rCVZLfKqTzlXkkT8G7tBZbqW17xPywMDqqp9LtzycnZGGoaUTWJKCYCgUKSCnTTH2ojEW6hubiBtsg+dKFgoBb/pg8QTuWDlAWjo0N1AZZp0x1qoTO0l+5Qs3IDXRmhD7QKxMYWf+7+nC63AzvJT0ZWQ34ctUtexPsaVjVkDk/xa8eDH1l194pJj775h7xvTH9lND6bDIKt037Gtd2zaNYrkULDr3JU0IM0FH1uORkC7C+rZnPdTNqLywHQpUtxbgAHnYQ/itReOfRDly5V6UEqkj2EUgOWMZjrDCcSG2r7Wv5S0tv+sGbn2m66c83rqsFzrPz4Uz+O91buLqqqPUBF5T78/jQHEjF+l0vS7Wi8Lz6IlV5CqOUjWGaYKb4NJBOzsVR4zHkU4Oj5d02BLg9d92sYB2WkcDSXDgM26zbNmo+M8AOCYlxZJqzBoLCaUtJ4okIcbL5M36BfqDWk54kDCV2oBNADNHDLQBLg0cdmRBgt26nLLt07oeX6ePnUh75238aKpdfcbvyO2frdPL2iHCcAmlRITfDFxDdojs0h6CRZ0LuRfc21vKF4D3fPuQqAD+/7PR/d/3sma6Pa6SrBAVWPoSbhd6fxoJzCj6hhmaricyJ8WBpSOvT7BK4AU0LEUQjHolMboF0fUF3aQLJXS25PSHV3Zf+cP8Ss8n2f/PGlL7981QQwLqL8hY/+t+WvTppKk+hZhRvIF2UzE6B44FyUsrCyzyKtBuyiEqyyGpQ51t0nHBsz3oPZ341wrHzlLQTSF0CZPpRuoIZaXtIXwA3HkP7AqMgPn8dV+RgzJUCBqyn6Q2m6w930BLpImoPYmouGhWGFqVFhKg2I6hJDk+hIDCXRlEQohZASpMqvvO0oqUlh645MGa5q99s0RlyxsVSJ7UVS7i9T2QMLRNMgtwy8ZqL+gx/9o5w3Z80R647e3dNZ1N5DDV0UkeQ7ztv4ufNmPqG5/EmksJVOCXEu115CR5FSfnKYSDRiIsV00cEKrZEKMXDYuR2l0aoqOKiq6ZL1pFQttirGJUQlEhw/tvTTHS5je22U5uIgPUGDjJGfjFUKQa9fH5kfVyhFUDpEHYuYkyNm2URsm4jlUJwOE7UNwo4i7EiCriTgKPy2g+k4mI6Fbjto0kIpWzma69gil82Yqf3vWv35hScz/+//+PufyyzuOT82exfNuy6kpWUBmGnMXAk+q5hcoAvHTAHgy5ZSkyhjlv9FfEXNPFo/m/+rvo6+YMlIHqw4uJXvHfg6k90OID/edndoChti83kxtpDnixbTGqzGcGzcnAsaKL8J2mHxfiOE0knCmQThTIqwlSVoZ1XQyUmfYzuG4yaEK7uV4+51HHcrrtoeymQayhNde8r729M33bnmlI8l+OFX/kEWz2oUVdX7xux3JLi7XZzGT9HkLOUvK8I0Ts43nPy2RCjQXXAMhY6NrRvYBat3CSXxuzZh22JJVwezWy0i3aX4s/7DxNoQGYr1Vvx6H0JPYguLtFAkNUGnprFPi9GqVzLoDyP8iogvzZzQbpYWbaG+6CA+c6wGK9fEtcK4VhTbimBZIXJ2QGUdU+ak5mSlSmWV22Nrzl5lZtf7A30bQoGBXUXB1IHJQSt92aV7X1Ox+eFnf+teKSq1Hj2BiN5NcsWTOK5OfXeK9dXT+SzfRQmNW9TNzGI30tWY2p7iEetavjLjUwDUb+xjXs8LXKe9yPnadiZrXUf8r4zy0+XOYIvzFp4OLeORyUG6Kvw4ocMdv0Iq/LYkkpVELUVRTlJsK6KWS8hyCDg5TCeH5malLrOuLjMZJbMDSuXacTO7bE02ZgJVbSKi/9vuUjWlz+/XLF1HApqSGMrBkI7yuzZ+xyJg2ypg2zLoONmA4w4U5XI//epn/usrx5OXxy3Kn7/t6+4T0xeMnflAZrhw+zZiXWHc+C5yVVMBqG8uJRs4C4A9ZRuY3lmHk9tASG0h7ivFDYaR/hBuKIwyxoq0HBpSpR1DgLgCcoZJzvCRM01yhomtm1i6Qcbnpy8cJRkIkvX5sAwDV9PRcImKQcIihYmFgYOGRCBHHrh8zgjUUAibGkqLGtpXuK0QoAq3YXFvE7fe+LkjN7ZPkK+v/qx177LzR3xAmm6jIZnMft7DHWOm15RS0NBwBYMDVejKol9FiWmHz3gtZRDTiiFdH0llklEGKaGREQKhJGVaC/X6QSaLQaq0ICFCSKJkCNMqTPb7FAcjJoPhAOmgSVewnL5QESl/EPdlxOJkIJREH2pUTU5188wN141r/gNc+PBdanRKTYWhWyxmI+fzLLv3z6TBuRRb06kZ6KVqsB+hFJYTQsiz2TItys5JPhKh0XKtuYo37OlmVs/6fAWhJJroZ9A3QFYTuNIA5UNIP0KaWHqAwZLt6NFdLGgPUjEYYDBURHe0mq5INZ3hGrpC1XQHqsgYh1sUx4JQEh0XQznouOi4aOTzVUMNPSvDpT3/zBQ+KULlY5tfvOKGcc//t93zM9UeKCtIrKRKa+cGdRf9BxXawGwmT26gqChfqXdueRs/K3k72ycPxQcpeVhj/njwOVliVpago/DldAIZE78tMB0Q0gGVQSkLhY3SoLs0QEdZMVnj8PikmBqgjG6iJAiQwcQeydvhOia/rp42lNMaedNBoEb2CYbvyvDxw3fm6nWbnc9/9j8OGzb5avj4b/9bbi6fNua+xtQgi/u3Em+v443FT1I9eTMHu+pZrnbztap/4glxBQvdjbyn81fUh9sQsXyYabynnH9Ifwt3ShTdUWibetF7c6wmyLl0sTVyH0HRTToQprFoMgfDVbQEqmn3FRHXIgwGysekzXQkugJXA1t/9YOLNOUiD1/7/Ji5+sDj6her/vm4EnLcfcqpoF80GdMO2y8WuXx9+zaea03nJxS3TRZsf4D1S8+iM9JMV6CFmcZSXHM24QPrOG9wM3rOxdJ1HF2jL1xF09ypWDPA78vg86XRdYeugUrasyXYDvSEy+gqnkQ8Usyg4cMy/biGH8c43GI+FtKcWIV1VA6pfur8feN7fkAZxhHzfzdzqVLtXMc9ZNqq6NXD1FXtY96sZ1i34U240k9M5EBBQAbYjZ+AcKkSadAyuIF8Sz089Co/5Pw5LcbBgEZPcJBcJEdbzKQlWEKjby7d+mHTb49hrrOds7W11IpWwiTRhhpcpfQSJo2V9ZO2YiRklIRWRFKLkNAjJLUIGcNPTveTIUSG4MgrR2Do5ccumM9YCQ1nqCyo0MnxADbpUw97UHczlz/ybpg69thYLkUgvZt4QGEFykbKqZCKSX0Oy3fnmN5hE7A00v5Z5KJ7kUYGSRnhbCUl0k8s2kukqJNgdD+BcB/+UBzDGOq2rBuOEWgFto/5bwUkVZQuquihgj7K6KeEQYpIECNJhBQRMgTJDuWlGrouJTQcNBxx4vW5pk6OsdYRKOXQZ6CJGXSlZ/COjQniJdsZHKhk1uznKa88wAOLitkuAqAcirq/zRV9m4l0fgZ/ejqtdfeQDr9Isy9EXFgImRwSRB2l+VHChzQqyAXPwQ7MwzWqsYwAPUcQ2FGOvjx8QKUJkEMgiVPCoChikMNmyh03Lhdbx/2c8VDoiHXQ5srFUAm7VDk3s43+ztk0xgI8xaUAfHHLL7l4cAMKaC8NsHVeEcXlPbyh4VG2dV9DT4Ufd1kZRR37eSLTy1+sVtpK38iB4qlkfWWH/d8w07rirIivY45/PeWRFkKhOIFAGlsZJIgxSBFxihkYeg0SI0mUBFFSREgTHvMMuAVlfvg5r1LtTKaZIuL4hwwfFx0LPzlG66csAbIF56pQx18HHbcoVx/otD6YXjOS6v5ASPxl8sViu7aQp+bfz+xunT2de7lOfw5t0MfydV8jdM1O3FQpbeWbuMhoIFipQElS/dUk+vykozb2ihZm1Tcdpq1l9fvYz3U8zDUMiJJDkzOGgMoQJkmINEHSBMkQIcEkDlJFJ8VOiqCt8FkarlVEMn42g5laUpqGLRSuAClADKuryluKwy9UPt1CSZRwAQchJAg3/0IihFSI/DGRwYTiLcebwy+P1j+Q/ODONSNjAqMipj1bPpV1pZN5Mnc1X2i4k3jSIqFK6S8y8IXSXHT2XYR21uEM2IjkFPb1tzPo1KCFQjTPXcHeylKifQkivf1MdTqoiLURLe0jVhQn6LPw+9Ok/D6e5FKe5wYOiilj0iSUpIoOqmmnRPUySbQyhSYq6CLKIAE9X4ilK3AyOk5Wx06axJuitB0ow80OF0OFRpwYcYYvUAmFf7ZNcLZNSUmcqD+Jro31qEolSLtBMkOvnOvHcn3kkgHgHeN7A4AP7LpvpLfRQBdusF48XTyLpqjAQWeKsw8rGaE3UsqgP8ygf8nIb33ptQSTj2Nmd1A6MBkzfR6SWWiUEMmVEs4Vk402EZ76LOVVeyku7kDTDvcgS6mRyURxbD9SaSipI5WGlPrIS8n8Z1NpVKk0lTKHUu1oysCQBroy0KSOpjQ0KVBK4A4JsYOGo3RcoeEKkfcjCZH/DKO2sUDJEXt5+J4VJPTSw1YWfdVcuX2Ta/m3j/yLCtZr/zt5MU3Bahzlp7jnbAw3xMHB6fzx4gzPhi8E4OydT1Nrv5HuyKU0VQUJqD6c4NmcE9dYmXIJuLuIhPbS7gNTKaJS0KdX046PfdqTJMTDTGnNMqujHFUzn8FZknTMJuXz4Zga0sw3ZhT50qEN2bVldFOXacPXp2ElozjZKFhBsuky+pAMGC5Z08wbKIaOFEMrjgmV70Jj+F2iqWEvBAiVr6k0peVfAjQtH6uhCYUQCtnfc5Qp7k+cs3Y2O9NjPSM1tRKwqWqytrFoEQA7xAIez1xNsL+YP9TcgBQ613Y/ycWDG8g6PlwtQKb9arJGjvDcZ3nf7Lv4/e4dvGBezmDxOfTUTOUvACwa+U/DdZgU76Eq0UONaKVIi6O5kvJAB2eVrsWsONwDqLsQzlr4c4OUWhlcuxtpayhHQzk6SA2lNJTUQI2+HDRs4cfRTFwMgmhM1RRhvx/T0Efn4FD5uyyUhpIgpUK6ElcqXClxlaSn9fgdRePSp3zjz36hnpyxhDq1n9XqJjoa3sSULVnCW7cTHOwmdXaEJfN2o8u8Q8YVkIga9JT66CnzkQqPtg16ZCUHtTqKO9NkEhF+OOOj9Gv5VlJMDbCUtcxiJxEShEkRIUGEBFGZJpiOoA8G0RIGJDTctI6TDeGqeWjGbPwqkg+2FxqakKQyfVIOdDvCtqRyXdcRKucoei0ldjk2z/gSg49r2YHGyx6//ZSN7r7js9+VF5hLxWUXh7EMjQ89uJ+PtH6VmNNOsn4xTZe04vjzfZqptmL6O/04YR9tZbW0GxbltDDDP0haOSAUU3wSf0HDKEGEP3Ejj3HFiNUklKQufYC6/mamdu6hvqUJI+Hg2DrpUIRAxCISTWAE89W12wmZPj9O2uBQd4JCkAhHGYiVMBDNvwajxWT9IaTht6Vm9AZTsi+WdooqsulwUFmaT8tJocssSvW5ltijUvozlf3Z+xc3tzZe98C3X9O+tO+85303vK3+Q3/RhM6Tqe9T9db1KAWrOwJ0Oia2fy6ObyoAvuxmKp0u6gNp0lIw4BrEVYC0qCSWCVJiV7GoppeV4c2E9NFKxk34cNtDZPtCDCbD9FlRMnYpugiCL4Tt84EmMDEI4CekfISUnwh+Qq6PQDaNP92L7nTjun30a2nidopMNmknhW97TpgbgUZgD9AEtAC9r4f+ZIAH3/9V+bV3XCf2RHUu37aFKxp30J/QCVT18pVrPowSGh91f4DvhQoc5+iTB4ZIs5Cd+PQsu/1T2M00QpkcuoJgKk3YSqNPT1JSfYCysoNHbiy5GnYyTDYRJDdgkI0L0p0BtIMGpYNZyhIZYraGMkO0TFlAV+VMckYZqABSSHJ6lrSZIukbIKcnkMaAY2iDSV0kmoTIPJvR4g9tKR5YmwyK7oZVDadMkNJXvnW/E516m76ndB5/EjdSn0xw4fYX+N3yy0EI/vvOW7hw2xbS3T6i9VmqlgygRSWPLKvBCNoc6JhO2+br2T3Jx76KGrKmDyUEQSvH1L42FrGJurJdlJa1outHCNmxwtA1hWSzQ7Y5juz30eOvpL2ilraqWpIlYaxYkKArCaeyhNMW4WwGM5chR5q0mU0nfNn9/eHkcykz/vSAv39TTs/tBwbe3vR2OmIlM5LKfFdJfOCqItut0RG4QqRShtHSF/Bt7SkuXtdZUbmppaK2reOSJa/KRTcuQ6KWHBz403NT7be16FPYzgLmL1yDf5dGR+nlTFaPYdeleXDeQvylbUN9TmJM3SyVoDM+lwfdt/JicBYf/dPveH7aDO678BKUplHd28u7Gl7kciNBmTwbM3c2TqAPqaVJa/sZtLoYyEra7SgdZBkwfMTMKjtKpKHUCNw7za3Y/2J0U3RtZFtpi6/T7fT19jjC3Q08/HobgnMofSHTDWYx5sQzNJSHWVsXJ57+f7wl9H14oRu3ZQn9l3ZRPm034do44dr876Zy8KjntByTkt5FbLUu4r9qzmVgKCivpuMAixvXMbO5kWjWpqu8np6SSh6fuYid9TMZjJaMGQYBgFIUD/ZR3tdJIJdBCY2sP0gyHCUdDJMKRkHQAeIlqWkvAi8BGzsuWdJ7cnJsfFm0t/GeVFUfUX8FJb0VbLGns9jcx3VRnd/1FJGSEun2obsJlssA762NY7iKjB0mEEoBKbow2CVns4wnCGr559kd9BF9WhFaD925MPsqS0kFhsZ2Cod5ms5crYZw+QIKF+hR0sFK7mHQarL67eZ4S6azqBu/v8AF9SJwH/A4WnjtTXeuKVw47HXJvqLwtou6nLP2RHX2VpZxTmcSp2cff5rzVpTQWOCu5Y3a4zTXLKHtwEJyykeF6CImkviwKCLBdjGLtArxImfn45/TECWHEJKS0jbK5+ynrOzgaLcBYA2U0dFajdUumdnUQisxumV0xFUQytlUDKSozfYRclLS1RjUNJoNyXPBZN8D981u2/PS3EdnkR/6poA40A40Ax2vp7rJzB28/YWOCz76ruZd3HO25GAkyn1nvRGEYEpXK78wzyMqOplJnMTBIAM9YeR1Qf7aZfO2yQ6Tq/ehso9hH1jErJ4elGYTjXVSXbuLktktY4TYShpk+/zEfCVEZT2x9hWIJxvpb1/L0+e/kcdXnkfa1Jjc30VVJkV9JonenYFusFSG1nAruyI9e/tKe3+fNbL3AmsbVjW8kkdhD/CNoddJZVxEOdoz/dNndTe9bWP1bDYk38mCyFYS73ApWfM4XW90UVGbAG1DR+cLbIYAGzmHnu4lTFo/i4bqErbWJZizfx/ffu+HUUPDQC5oaeUze9LU2uei5/KtXNtI0uI/QHvGoi8JXWYM4fNTY5YkJqnw/YtkdPUlX3/XxsI0LuE6PjYeF3uKEQ/4EmQpWdiXo6E8zJTSXh4PRWgbvIy6qS08Wb6SwT0xylr6OKd6PRXFW5GBFgyhKEYyKS05cLCUiCYoiUE4NY+FnR/g9hkhfjYj31ot7+vk0mfuZWbLforTkvULFvPDK9+C1McWH105La7SH0KINBBFqQSQG4iVrIzHSusRwiXf+bkVWAvsArZ3XLKk87XOt/Hi0hfXyfV//1Oi/gqCehEbe65hYc0PWRxJ8ZvI1+kVkwlaWa5oeIHKZD8dGZeqybsIhPJ9we3tM1CuyQU1z6HpLnbaT+wvOkXP2nQURVhXU4Hly/draWjMKTqX+cXnY2ij/ehZMjTpne39wrqrt/npZ9J2z0eAy4BK8ouIbQV+AfzupjvXtL62OXTyKbdzn5nf7tz/8xl+9pdW0+6vRtVpNEw7G4DS3FoIQvWUTSzqe56lSQN9aETBTqbxDOcyQITuohKK04OEbBvTzFBT20ht1V7MQEG7JVNEtqmaTXuKSB4o5prmtTRXRdkUy09KoklJTTxBbf8g5cncZgHfBO6dt6PxsAlGbs+/NZ7c3HltyDrtPz1YtPCjmbXNrJzk8li1xmAoX0aXH9hFxv8iT0+dSVtxB2fv72QwaPNvNTaX7LuRfc4mZsxYz5SpWygraUfT44QiY4d6ZxyDTGOY7t0lZLuDXFh6HZOK8kHEyYbfcfvcUp5/+2eZ3NHEG3avw/SN9vlnRZodxU20hlv7E77EbcDPG1Y17H7NMuc4GRdR/tStb27b+J2fsrEaDsh5RA9eSqL+MdJvyjc+jDZB+HGNzKQp7LRX8PMLVxCXNbxpbRpNF/z2XB/tZX4gTHtFNQCLu1p5136NK/oiaEM9jNnIAVp8B2kZiLI/ncZEpzpYYZ8jI0/U2qVfr1Cxp+tWr3zdtC7HAzvAHmD52X0OvwFeKFnCJ83/5b9if8PG2GIg31XSXRnkiaIOfLlOyJlclUzxgbYM9+w/m5pFPSAFiedmsLTqfXxpSYRHqvOu6iXbXuTdD97NtI52ts+cxzc+9RHigWIAqlOZ7R3h4I+B/cDG1kvPObr5fRqTtvIBfUGziGlN/ewoWcj8wBau4AEe6ruBxS27merbyZT52ykvz8/tkUoVkU6UU1XZjKbnvZD2vij1P8gyoPl4elYtqeBowEnMLOOCsusoCtbkj8320Z892BQXuZ9sj7+wKSczFwJ/C3xq6CcucBfwPeC503WMMkC5CD02Lekwb8ClsUhnw5Q5DMw5G6VpLEhsYoE/yX4rxBRfmq3zY3Q0DnBtIkqP/VF2iSn06a2Ewx0YgzahQJLaqdupqtyHrufd03ouRrB9CW07Bbuae1jS0skVyRY6iiJsmF6Jq2sIqagZGGBOW5yg4z4MfH7ejsb1E5szrx2aUttcX4Bep4eP7rXYVqTT7YOLdu2kPDVAQguwfnaUuctbWXdflDtnDrCs9xpM3aKtZT4VuUpCcx4iUjQ6Tlkq2J+JUbwlyIHNxSg0ApbLyoqrqCk6CyVd9jev4baVsyjP9nPZvs2gG+ALoFC0B9vYW7SPrkBXN4JvAD9pWNVwys9EOF4zelEx6FiAb1dUo+Kl95ANtmOXN+Lb76PyD/OR+7ajm13c8S8rqGsq47qmBI8tCrJtymiLpra7k8Udcd4cj3JuenR+81TpdnqqnqFl30xacn7OpTJ7Tm7WAyECv9MQd9etXvm6Gmg/njiG0QAsP7cfgtk03YFS5pe1sbhjL5uYgTs5THF5K3r6O0grS0BKbunpY1ZPhL90L2X6lfvp3VFCfG8R51Vexi1Linis2kR3XT7w1zu58dF7sX0G3/3U+3loxmUAVGScpKm0v9nwpvPvndirPzVIu/mpkX2+IgKpOH0Hp8CsLVziPkpkRxSlNOoXb6M83IqjDA7uuJKKst1UVOcb64PxcmL3QenaNJuqq+koHhu9O8M/h6VV16LpPqSVZGfb/WyRe0GIaeQtsUIGgJ8B37/pzjX7T/rFnwJc8qN/snbetIZP7DH5h2UhGupmAHmrdem2JvzU0imqmbT0PiYHbQ5Mns53D1zBsv5z6Q3sIOVvo7a4m/LytRQVj46NDQxMo2jfFbTsdtkcX0s0l+Lcll6215WTrasYOS6WzqglB7pFJGe3Ap+ct6Pxr691Hkw0t9xyS+aZX/7R3l9ebl4bH+CepyR7G+9m7cz5pKMQMafRW/ICWx/roKneZGn/mykamoFtZneEp8wdLHIkk3WwJWxJlvKWxzvJ7vOzK5SfVbMskWXJpLdSXroIpSRPZl5i0+IyZmTjgADdwBIq1xTbJffG9gYzRsYFvgN89fU06+O4iXJtztoRkNairOajKeyjZOPHifcdJLVvOy/UTmPJgX0E7Czf+5+vcdflf8vtl68gHQygScnbHrufS3Y1MXP6WykT+U5PiWSwch3xGfeQslKsa3gjZRRxrTvnvqiMrKpbvbJnvNL+esbVY88BHwwKP3OadrBp3lIeKF/JDzI/4PuT3oOZ6eCh1PNkNcmstMu79+o09y6jvTLB7Gv30fJ0DamOENFgDT+4cBGPV5noruIrP7mVC7c8z46La/nqDZ+jPVCJUIo3djkvbi/SL95w7dLXfV/keJGUaQloPn8xkcEG+trLaa+voybQQnhxMwF/gkq9lawM0rXxXdTNvQcz3IfjGuzZuYKZf2knYyXZNGcyiJGoC5CKiwLnUlObH1KS6d3BE/EHMoNa7uvkV+U5F5hCfmW7zcA9wJ9uunPNKW8NjDdpZ5ALeop4y7pN/OWcJQAsb24knBPkhoYo7dt5LjPnPcfk8g7KgnfT3LeeYHCQC0vbRoK2lIJI9xJKm68h3RbgqZ57ybgJpvbkQGbYMD3vqVAoHCMXP2fvQHFtPClE3ivxkXk7Gvsn4vpPBSKZVN/+uplV8YNd1BpR6qwEO1M2mbAOpo+52Tp82Zksjs8AI29HVbZLFj99O63/KJnsV+RcjYNb5nPjmi2sE5NprigGQCPE4vKLKC9bhJIuD2lbORiLU9Qr6auclOkP8td2835fW+jg24Z6SBuADzSsatgwQdlxwoybKJf6rR/XZ/p/uDtcxY6oxhWJEKGiOTw/aw4pCRuXVFDW+Wdue8tbWDc/H+o+68A+PvLn39E+/yIWz3wfAaWRw2ZQHySz6H/JVGwlO+Bj09ZrqVFT1EXOnI8GVPD2M81F/XIcLK59gf02mtCYeaCJTfOW8nDxefzHnv/ha03/w7trq8lqPibFDc59sZ4DUjDl0lZik5PsuXsKmd4A2UCQh675AJsrTQxX8dn/u4MLtj/Prz/wXu5Ydg22oVGUs/nHXc4fr29z3uXl/1hSMmsBgYBZRK7ThQpB286FVC1qZWFoHQA5N0B88/VMWvQ7dDNHKh1jx/Y3UrGxF5lNsHPS6MhwAZQncpxfdhWhSSsA6DnwKI/b61qkxvU33blm4xGScUaTc1I5wP+h9TtZ8eKLbDnrzVS0P4HmZqk/7yCBiJ/Stf9Ksn4ngUgv4fAA4fDoTHW92QgDDUu5LPVmfFYF+/vX82L8boQSzG/poaUsymCoGIADlQlZmex58m3Py0vIr9r5OeDbZ/piIQErt3f3pBlVA3t3URuagR6dhDPwU4Khj5GOtTA3MR2KNRAZkBrNwU5qeh5HhBTL6x0UUHqvn6kP7mRDde2IIO+YspBVzRYVtecD8IRooGtgGzMSYc6f/Jbkz/13rnqk+MlbgOHZ+/4H+HzDqobDx0m9Dhg3UY7JyB2TBjM/3B2G7UU617eZbDPbiM+t58+lGgPBeXQXLcDVNXTH4ZwdDeiuyx9u/DTf3SLxKQ2lFKbuklj2feziZuy0zvaGiymn1lrhzDhn5jevbBiv9J4urCv3t0tloQnB9PY2hHRpC1fzjdjf0mavZ7cvTSCn8YZ11QQtwYzzDhKYnqLpgXoyvQEGo6X85bqP0lkcxu8qvnDnGt6w+TG+/bff4t4VkwCY3tfHdxvMR2qy/I0nyIeTkol2YJpfD2P2pyGQJj1QTlvrHOrqdgCgOxrVS+8EIB6vonH7GzE6shR3NLG9QJBNVcLi/XuYPO9vMasXo6TLwb1/5Hm9aTuauOqmO9eccQtOHAuWa3UB9XqonLPWPsCiPVtZP7WKhD+L7LYonn6Q7EX/hkKQzsTYI7NomqQrMZWnm25g4e40ny+Zj+aP0DGwhRfij6BLxZTuOI11ZSghEMrlmYV9/devTfx+xS71MfKC/N55Oxp/O8GXf0pgSrmpo3LSBQPuSwCoaAVT9ya5bdn9nNO/DGVaICQWLs/XPE1PqItN12nU9JiEAzbmfkHsQYfmsmL2VebnpNi6cBFXbelmSv0785+dHbT3reWy/RkCK67LfWrqLQ+1+rt+D+hAF7CqYVXDAxOTA+PDuIny9bd8Iv3Q9/4MNbCxRCepw7+dW8/+WOiwY13DYMPcs7i4qYkvb7bxYWJJhalJDi7OC7KT1dm2biW6VpMN46tb8M1rXxdDZF5r0oZI5oQkiE7Etanr28fB8lk8rypprsp7Mc9vKGNWR4bJ89pJL3VoebqKwYMRBiNF/PG6D9FbHKEqI/nak3up2Xs/v7z2P7h3RT7gbun+nfzXjtKuIsx3161eecqMizyVyDj9TznSnmZoJnNzCX7EYs41u2nat5xkopxZs1/A8A9FW7fNZu/e5bSlg1y49yX2VRbnT6Ik1eYsFm19keg5H8aomINyLZp2/h9rA92bEeLym+5c43XZHIWscncA9WawnKCdpUf3MamnhR2TyonvLcW/yOLnO9/Jjr45+LQcZcF+ejJl2NLk8oMv8dlAHZo/QjLbytP9D4FSlCbS7KvKi4MSKe5aGV//pT9kfz2tk/8e+tu/9wR5FAP5vKsbf98SGgpQDJczpQtai3fSXL6LymwlEklndhq57huozpQyy3UIXboagCm7M3QvK2a7XY4AehZVUjLo47zQYoQZpNfpZmvXo1yytwcu+jAfnvmVeEbPvW3o7+8C/r5hVcORJ8x+HTFuogxQOWApQOwPa3xzQYD9MRO/bTG9u5V9FbXkTD+mbTGv8wBLD+zh/YmlBDDJSEVQE3TO+S3Zim1IW7DjhaVktMkSLXPWe//t3zxBPgodlyyxNj/yJEEXTOGnNr6Wg+Wz2DJ3HrEeOGtvgA8+1gkzHHqvd+jbWUTP9lJ6i8v5y9Wr6Csuoi7l8pO1GTIt9/Doon/nDyuLQAjmtjfz8d05igh8wuvDPzrV8Z57MpMSq6K+Uio0ne29Mzg/qCO1Abq7p9HXMxWzqIe4FaFpcAr9abiheQ3dsSBCKTSpmFOiUbt5B7HzP41ePBllZ2jedjtrY8kGhLjspjvXeM/Ay5BV1ovAFYFAGQ6wp2gSZ8X3sAPIJDS+9NQXsEQ+sMiSftpT+UbnpS0v8tmuA5jLLsd1LZ7svg+pXKKZHN1F4Xwns97Hby7N3n7bD7J/ieSGJpuCf5+3o/G2CbjUU5aqRP9GTUp2lUUhDWagFEMK3t/q8rRWxHLzIO+2+vnnzPVsUDNIkuRT9d9gwGfjz7m0qWqecCfjw8Y/2+LJKW/iZ7f/HmPFJ5HK5YWe+zh3ZxPZlav4zKz/TmT0XBWwD/how6qGRyf6+seLcRXl6lTC8kvbn9NMHqwx0ZTiM8818qd5eUGOZlLcsOlppmd9XGSdTYwglsxPqz5Y/TzxKY8AsO+5mfSLOYSU+YnPfunLe8czjacjCVNQ5oKp+TC7t8DM95ALzaEyXspn/tIFFdD/EZ1Ul8mBp2rpKyrjtzd8lEwwRHXS5cfrMxT3tfFc5L2sWR4mHdAoSw5w3e7dLHbPfRT480Rf46nMWQcT6xKz+4j6StFiNczuP8hTWhGLU7O2tevaC3HBb7t8nX9uG6iIBtwM7+v6A1kzP4ymJJVhcGYFJXuqKLngSrRAETI3SOuW21hb6jYOWcieIL8Cabv9UeBLQbOIhGYyOdHFvfUXYmjdBGWW0lw/rtC4puNhpCYYNGPUpfq5dk8TgYu+BMDWwY0knT58tkMiFEAoRSzXxc+utr72629bfwSeJe8mvQP4fxN3tacmAcduDtg5GupqkTslmmZCIMbKfX0Ey4LMpoQpsU6+Zd7O59Of5//5byVblwRMtnUtYE9/DdWyg1BlhntWvJsP3XEX4bnXA7AzsZGafduJzb6GT877pZXSM1Hyk71f0bCqoe3l0vV649Uvo1FAiOzAje1NI5+va3W4OFvDjopydNfhbY/8keltEWtK/GyKybu1B12JKjpIx/xfANCxoZIOZylhAuvTwvrpeKbvdGXQN7RUphZgSquFmc0vTODYq/jztdfT/zmTrHTZ9+AUXDQeXXkDmWCISb0Ov3gxTXVW0WBH2Tw9xJ5aH7p0uaxxLVdk57s62j94/cgvjyllR8LK66YWrWFx7x62i0qykWdnf8j9/MN6UeOv2gYqogBXZh+lKJcPMKrrG2T79BmUu9cyacFb0AJFuIOttL/0X7xUbO1VmrjspjvXvO7dca8FXZnmTZabHxBgxaqpTfeytWIGLYH8aI43dTzIu9r+RJEcJOymcZ2guqyxkfCi96IFixm0E+weeCa/opdpoEnF7I5OHlrm3vG/37VuA+4lv9LE48BHz/SgriNxyy23pAK2pZrrpo8ME8wWVeHbrVE8bZD9/hJyMsR07SB3Rv6eurKdDBSZuErn/vSVVPd3IEyXzuUzCOyBi3okelE9tpulo+VR5qgqblu+zx40Uj5gB7DydBNkGGdRDurW/mt3SCarJnSp+NieLMUEWd7rsnJLF/Wp6yhVC3xnBfMzFHVaklK/S9uiH6IMi8GDYfZ1rEAYfpnBeuctt9ziFfxjIGHmh3P49AB1qQBLnCcAOLhoAedf+jA5J8e++2bgZDUev/DNNNdNx2crPr8hRbkD/bksDRGXB8/Ojxlf3tTI8kSIchX7ad3qlduP9r8eeebtaMwl7Lwom+Fq3tienzPiz1xofiLypd+tz82pBrgs+zhXD6wjpxmEsxZdsQiBaTdyUXgKQjex2zfR/fw32VxqHnQMY+VNd65pn7iren3xD7+7K56w85O4ZCryQnzD3meIpfJhEH5NEIi9HzP6bpqKrky9e+uzIla1GLNmCVJJnu/8A65yUEJguC7L97Wxdrbc/5XfZP6J/LSkdcBO4O3zdjSO+yIPpwtB23Js08+gys+331o/Hb1PECp1cWSW++Ofo9+pRQE7puanh31KvpHlLz4BgKiLcVfsb/jEXb/GN/saAPYObGDe/oM0XLScZ4o2muQD7N7XsKph/JfhOwUYX0tZGuuTqRgfsn+NIbI8VZnv8F/VZKXO3Rf4WH3QuWtFREMXgg5LEjMEPTP+jB3uxEoa7F+7FDdSTEwF7/i3r3y56RX+zmOI5NDMQz4tAFmNTwafpEJ1kjb9/D73Fnb+YSZ92Qh/ufo9bFywHCEV73gxyXIn3+Z5yejjvuUCyzSoGOxnUcseltuzcsDXJvCyXlcMOL0KIBCupj7ey8JMfmKQJEE05XJV70P83bS/sjObn3SiJJWh+YK/4SNdJkLTsQ++SPal22gqiXQlQv6zPEE+fpL2YAbAjORHDSzr2sn7Gx7DbztobopM8rcop533bn08XK7At/jdAGyLP0vczs8kFcmmecOuFjpKXBlOVp4L/Jz8ckUdwDVn8jjkY8G07SRAZzg/xaYIlTAQ9BNLpehjCruie/lE/yf4Vf1yUsUCWxnwnCKQsVGGwdq6y7hw41qmaBXoJdNwpIW752ECU9/A96bfOzzE6dsNqxrWTdQ1nmzGVZRLrfLHe8myaMDmK3yexyoGcASc2+eG3x7RVy8LhN9uCo0uW2IBlOylf2o+er312TriJfX4lGHFtfQ/jme6TndSmisBfHoEqcDqDPC33AHAA5Hr+e3lH+D2d/8Te6bOw3Bc3vpCiqt6kuiaTq9r88DUTmdf5SSEkrxx9ybmujVECPy8bvXK0841dLKIO70WgM+MIsww79j+AEXuIJMzB3hn25+4ZPZzDLxQjBQaxakMB5dcxz/0TkUTGnbbBrLrf05HTKh57f1Lb7pzzetm9qFTiYzM7gcwI7W8ODs/3ltXsKy5A6np6NIi2PcgFb0NBJZ9EN0XIW51syP+ApqUmLJNXbSzHXDoCVf825s37b8KeCv5yVneNG9Ho2covAKG6/QB9ATy3tCgHqU3GsTXAVpllLRZyqVLnqFmaj4rm3Yto2hPvp2TK61m26TpvO/+P+Ofcy0AB/rWUt+X4s9vMFW/MegnP6XvV177K3vtGFdRrtKrt/TrA/ji06jnIB9N3sKOVH51Dx2tBGB/TtKUk9QFbNrP+ikIRd/OIvoyC0DXKZexn9xyyy2J8UzX6U5Gd10Av55f4rL5hWVc/PAyVmx6Nv+5fhZZf4ipbW184NFBFhy0mKXn+6EfrnoytW7muQbA2Qd2U56Ms8yeoWBk2IfHMZBTAx1JO1+56EV1LOhs5f37f8UNHfcys7qZAbecPVZ+PLLPX85bfcvRGBXknK4oSzifPatx+2m3YMRrRU6mnwHwharYMENwsCw/TVdxOsfVB/fKZ8+9gim9CYzJF2BUzseRNi+1/QmJxOcmuKgxIwD2VJYeXL6v62fA94dO/ZUzaR7rV4MU4iBAbzAfQxw0ovRGghitgsmTNjN97ovMmLEeIaCldS7de0rRrBwYkFumceGGdVQY5eilM3ClQ2jLGuLLr+SPFY8Pd2X+c8OqhtREXd9rwbhGXxua2RoQDumuuTDzHkJ1fex4qYU2ZxJubA8yMTmrS1/g4qhB95zfjLit29dOI11fQlD57EEt/a/jmaYzgZxwLcD0m/lK3+7q4tmuLi7aBwt6dpGMRlm8rgnDdwnJ2HQmufvx+WaS0NI8XDnPl/EFKEoNsGz/TqbKCsL476tbvXLPxF7V64uidGJD3OqaEjFLEEV1hHt2sqCyldz5Dhnh7+j/VazaiUqCDsyf/z78tg+nr4nsup+BdDhQrTXN6pDfnujreD2TdfrvBj4cClQw86DJrW+S2uo78l07Ro+rXb1p4w8m9ac+5V96HQDr7B30qzhCKUoycfwO9EQCtJYWv3tRS99XgSJgI/AfE3VNrzcygeBO4OLOobihkB4lHgpgdAjKKvOON6kEjyeupSLwHOFsFghSubCbdQNX88kHf405Oz9RSH/3BqJ6lG8t2qOkkBrwMIwMSTttGVdLuW71ykRMl6qtvxIjU4YwHMpqHicjwR2oQ7q+wDlhHbt6PfH6xwA48EQNVvlcEIJqWXzXv37l5tfl1GgTia3loyp8Whhdnzyy3/DVMH/PDD52x5+YNFBKMjYdSZbpwfwYzS2RprattbNMgAv3bUNTLufaswG8qPfjZH5L5oG4le+XtMqnAjDpaSs7+cHcvw38dorfr6XQ0Dh78o1U2UVIK0V27W0gHbZMQU3qlcu8iN5XR1f2wEMZJ4kmNJb0T2dfjaKtZHQB5DesffFd/rrlaOEKUkKS2/kXAARJzm5ycIVgV3XZPdds2ZcCPjL0s3+Yt6PROfzfPI5E2uffBdAazEtLUI/g6BrpzgDSEaRzAbaxgGWBh6jOZqEniNAkvt5Kalu6Kc+5GLVLAfBve4Bd55/LC7EtgvyqZ//0elpj+kQZV1EGCCmRbZUZIq0XAFAxdwfRXK9SKsCykE4s2kPHgp8D0LmplGzfZJIhk4AyVUj5P/Vy5/Y4Mq7QuwF8Aozo9YRdk2CRzQXBHhZt+xnZQDl7p70ZgOpkK6WhCBLJX6qqy11dp2qgh/q+LqbLCmIq2EM+2tTjOChP2Vv7svnYrED5fFojlRg5FQi8ZHx1Ucu+koFIhBUVb6LePxUpXbIv/hCV6aOtBJ6Zr391WYMXQPRq+cSvf5MbsPuSAMHoTOq74adXGWK4FheaUemf9xYAeg4+TkckH4h63u581u+qLrUGg76/B75Lvkv6znk7Gp95ba/i9c1AMLIHoC2Ut5QDRhiBIKEHaLxtFk0vxVgotlLss0luGZpetlRnX3wG73r0Xsz6CxCajtW/D80X4jeztgyf+icNqxrOiJEg4y7KAUfrtfU07W3TQeqIik6WLmsU1xYZlBd107L0W0gzTaozQNvaanKTZkqAaW7lY+/8+oe8SRJOAKGF9gIYGgjhY0ZfEbZvOuZL+zAcm4YF7wPNwMi1EKjJPwhd+uC+ZydP8gGcs38nUrjyDdZ8gD/UrV5pH/XPPI5Gc5u1j6ybxm9G2THvap6oW0zWMGitn84Vte9ncmQeUilSL92m3N7dSAE/udpIP7FEeFHu40TazTQA6KXT+fCDLjsm28bGKUEkYExajhYsQab7ONj9FAhBaTJFccamvShMU0XRR65uaFoGXARkgM9O5LW8LhGiF6Aj5EMphSZ0AnqYtpIIkaxFri/A44MGzx0IMXggggLC3cuUIS1i6RRi2hsAkM3PsOO8pWwN7QHIAf8+Ydf0GjP+lrKjN+WCPWyy02g789Zy5/w72P+Gm2m68AvY4S6shEnTQ3XooXLSptQCymSGW/Xp8U7LmYKj+5oBTC0fvJWIzuDKR57An7XYNfNqUpEZKGXzzMwXmOLLD1V4sDRTYhkm0UyKuv5u5rmTHD8mwO8m6DJe73Tk9JTan9wGwIVlU0iXJdm09DwWzv4gxb5KctKlbecjHXRsEgBrluvsmaQ+17CqwZtTfJwQyv0RQFF0BjPbNL57m8v8tgya0PHNugqA/rZnaS3OT140r62PhN9ka13Fbddu2fcr4KtDp/rOvB2NBybiGl7n9ACkfX4skff6h7QI/eEgQctG6wkzpeHtlD0yC4DewCxq4vvERRtfQiuajC9chXJtZC4h/zB9ZP2hnzasajhjAiDHXZSDrrZV1xxQJs80FdO1sRwlwQ53guaSaA2z+y9TyNkR7JrpNsAMt2rnOd+8vnG803KmkDJFM4APgQZ0lS9GCo2ts5dxsC4/tODZqX/FX9xLqRsjK6z0mvpYCcDsroMoXHeFNdtHfh5Zz113Aszb0SiLMvbgjoGXcKVDRaCei6reySU17yFsFDFox3m+s7M/tuOP1QCpgJ+7V5CxTPGziU776USpv/J3trTw60HidXOoHICADebUi9BjtWRlgufZBcDU7jh+22XD1Oom29D/Gfgb8mOSB4BvTeBlvJ7pAbAMk7TIz7EyKZuPJ+6JhsCBwW37yNgWUgSZIpaQ1ePE0ilyMy8CwOnYQtfi5drm8E5QSOC/JuZSJoZxF2WfFlhbrMI4ZgI3XMTBhhmZ7b+dyb7762j83XT2rqknK4tQZpC04Zqm0pnl1n55vNNxJhE3RbOdN5LRjCyuGeHRi79NV+0H0NDZUfEijTVPcdHAuQDs1fuSzWWVAMzsbGGqrOgYspJ/Ubd6pZyYq3j9M6M9uz3rJNgaz7drakLTMYRBe3ofT3RvUYvW/YctUCjgu2/2Y5t8q2FVQ3ZiU316seC/32oP2oMtAOlpK9SDi2u7fnZVgMziKwFYm3qalO6gSTuLUjwzpz6bCvjefu3mvZJRF+l/zdvReFrOFvUaEEcpBdCrZwCYlDNHpi8FQCVBRAhE30Fdx2ZmtTQDYNaeDYBMtDoPTN2fP1ZwT8OqhjPKYzHuolzir3qhRIbzYRJCkKmbaiop9g4eiJIb8GPHynB9AaiZkQaY5dYky1X0rvFOx5lETtfa+obmv7Zj+fKrKx+g0VS6iaem30mtCHBechEAj1ZYlUrLLzpRnEmoC6x5tUOn+r8JSP5pQ+1A5sVkSLJj4EW69j+EZafZM7iBZ7v+zFk7Hmg23UwlwOZZC2iYlpYZv/juRKf5dCRsxL4JUBeaKVyClStT11JCGVknRUfvdoA+qZlGc2UJlqH/0013rtkI/DMwFWgHvjNRaX+903HJEhfoA+jX8+7rgK+IRQe6CFgOftshoYV5vuYGBor6KO5+koBts2/+hUT1KMqxcCtmGY8UvTBsHJxxK3GN6zhlAE3o+ypkjF20I5Aow2fUX98/I9MTYE/T+SjdINiyl4xPhDQlmOvW3l63eqU35OBV0O0XLX1+QVVO4QbiBMr38kRkEz3hFjqiTZzlVFGcqCEkAyRFJvtETXEAYFpPG0Gh74wQmAs8Vbd6ZfPEXsnrG58rd3QXp4lkojyXW4u29yUyfpPyhNVZMdgzDWAgUs+t1/QQyXDnM59s8AIbTwIB3fc7pdT3iv2VekVwnjqrZKUA2D7wPFK5AKVDh/4B+Enj3HkzgH8b2vf5eTsaT+vJKU46+WCvsn7ThRyIYAnlyTSTGpMMBP08O0vnnIE1VPX0U5ZKYxkGe897G4v7wI3vd1+cntRTekYDDgAPTfDVvOaMu6Vct3plrswJpgCUGgo8SlTQPrCoR/kCGAO92FVTEwBz3FpKVfQH452GM40dRXpX7/BKUSLEdc5cztVKf5sL9fzmpvb3OYvjS3lj4hwAduld/tbSKgCm9rRxTfacIcc3v56ItJ9m7Ajlsrga5EyDjN/Ebzss2d9aBZD1FfOrq5YxEOplIDIiAh7jTN3qlX1CiDsBLq2+XgT0MBknkd3fty4/OirvXr0D+MC1m/f6yZf9IPAE8MuJSfVpRQ9Ary+f3SJYit+R2LpOUSZH1UCK4t5ulu3Nu6h/ee3bWDGQtw+FP6bfX/zMcODj7WdiEOS4izJAyNWbw8oPIl/fHzxw1t5ksqwcJdFzWeUE/FFdaSxyp7zozRz16um4ZIk9YAwvShEF4J19V777N7tXn3PpwLnG47G1mXOTZwHwfIktXN0gkk0TyXa3l6noHPJz+/5xgpJ/OrHt7H02jy3tBAWm47KsqQOfK8mZEZ5b9n6en/4INX281LCqwSv3J5evky/XAASN6L9cvm3f9y7f2sTl25r3Xrt578dvunNNmvxUmiuAOPB33gQu40IPgG6WAKBCxQDsm5JfjGXxwW4u3NOO6Ur21dbzxMrrmOQGUUrSVaLTEN6tAwr4xQSkfcI5KaJsav61C5z6kc+OE5gBYPT3kKusSwEscicTVUFv5qhxIqm7CiCiYnRp8eHds9eHG1NnZWYGfcokLlL2C5URAOr7Opnj1gyPzH+gbvVKL7DlVTJvR2PPvJZcVzxqc8/KOBXuskErPI8ds27kmRWfVb879y4sI4sS6u8nOq2nO3WrVzYC1wBPAp8n3zd5i8+VHT5XzgR+3Dh33meBD5MXgHfO29HYPFHpPc3oBXD8xTkAPVACCDpC2azlD7qGlBhufga1z376c5w3PG2OdNWDJc8On+OhMy3Aa5iTIsp+PfjcWW49JU5gdKdSOMXlDroeKZIhFjtTs+T7dDzGAUtTNkBYhfhL6DmyWH3Ac9+qvWP9JUNR17v0dvNAeX6Kzbq+DnVJdvHMoZ97Y5PHCb8rt87bb9IbG+A35z1jN8x/zz80lzgNa+Z+V8RDnZzVrPY9/M9bvcUNXgPqVq98tG71yovrVq9cXbd6pZy3ozEOfGzo61WMzmn9tXk7Gh+ZkESenvQAHAiLFIAmdIQ/RjiTCWQWv01YuuBASYS18xfTW1zGJa35Lnyl6+Kh4ueH16r++cQkfeI5KaIMrNfQuCK3EKO3Ey2bzruyNc0wlZ65wl6Egf7nutUrvSXqxg0tBRCWJq2+Ln4VeHrjNfP+/j0+ZaxclM4P1N8UGiQViKJJSWWipdNAn0F+5qK7JzDhpxubrluXH+V0oLK3rMe69Xtbq59a2FoxgM+GKzfImyc4fWc083Y03g38HdA29PoMcMtEpuk0pAegLTg0Jop8sFdp0uFFa4P2xPzpMmI5PL9oKbpULBnMr+bV5G+l3xj0AQngnolJ+sQz7tHXQ2wFrJge800e5JtdXdvvS85dFtHR570tt+LfogSDMLTgr8e4oITeC7KkzDHIGBmA8zWlrbpkYLnQ0GgX/WyuiAFQM9DDfKtiO1AN3Fu3emVyApN+uvHE/IPOv0xvM3P7apX/rktGJyK64Xm3+7ydyuu7n2Dm7Wj8BWdof+VrRDtAa0gMW71ooVKqepvZUZWjZiCllaSyvLBgCXMSkoDSNIC/lDzeD5QAf2pY1ZA58qlPf06KpVy3eqUFbAa4tOY92/71zjXP3PKVrzzwd7lLWqIEi4FWwHMXjSOupjcDlNk6ptKVQoWEFB+8YuB8APboHRwoq3Ig3598aXbJjKGf3jkhCT59eVqA/NQa269J+gGEVLz9Gcnbn1Pf8AKJPM4A2gF6/NqI0eeGSwjnYJFdeffClq62g1W1dJVVcE6v4wBIFE8WrRs+/oweCXKy3NeQD7AAuLhg33B/zh11q1eecaHuJ5Mih+bhYVFTcrWiOdLMLKt+Sr1VjY3LLqOb7lilAVA50Npnok8BUngrQo0rQ/2WT9f1wn//1Pn11evk97/zE5cbn5ZxTXH7RKfPw+M1oB0go1M0vKO3pgyA2h0vXGpIVfvcoqVJgAt73AzAAV97wtacKNAJPP6ap/gU4mSK8nDGXgrQcvPTi4HLyK+L+ZOT+L9nJIv73d3N4fztnGTVsaFiA5fHzwOgWetib1lRl6MbhHIZFia03UM/u79u9cr0BCX5dOZbALV9fOqDD8tP1+SjS1fP29GYmNBUeXi8NrQBSCFitkACuCWTcgCaIgJw/wUXt6MU8wbdCMBfSx7vHPrtbxpWNZzRk0mdTFF+BrCA6UOC/Lmh/X+sW71y/0n83zOSGSnZ0hrMe0ZLRBXFVhEXDy4HYK/emWgvqioHqO/rYrk1u2ToZ2smJLGnP/cCvy/4/CL5NXo9PM4E4uSXW6TfRxdAsVFr3b8s78lLBYLPHKiqnVyVVYRchELxWNFLU4Z+e8bHGp00UR6KrB6OoLsbePfQ9jdP1n+e4fS2+fM9ArVuMe/vvH4gLIMkyGRbtd7bWkqrNIC6/g63RpbMHvrN/ROV2NOZoX7jvwXeAbwXuHjejkZv4QmPM4KOS5YohlzYB0LaAYBiNxq+4wqDj31KX//mb//sPxHCf0G3YwP064MJS7dNYF3DqobNE5fyU4OTFX09zE+AtwOThz7/rm71yjM+008SnUldAYKpVpCp1vlFAEF8/9gRjf17fziGJl1m9sd362hzgY11q1d2TWyST1/m7Wh0AG+hFY8zlXZg6pZivf2cfomGppU7JXRF+xYr4b4ZNC7rckyADeGRVXvPuMUnjsTJdF9Tt3rlQ+SDu/YCzwKfOpn/d4bTOSsp2RUZvaWDIvPXX/ifqNlTNbkcYGpvB2dnq4YXQfCi3z08PE4WuwA2lhjDc+szJVfTpcBAqesB5g7mPXtbQ3ui5New/u0EpPOU42RbytStXvkTvMCu14LuC/p09fFzg+Jje3JU9jazXtt5g63rN+ysyjsqZnfsl/Od2cNei0cnLqkeHh6nOVsA9kS14uEdC1Oz9j9f3F+J5qsKOFIV2QiA3cEDAL9oWNXgrc7FSbaUPV476lavdCos0ZNSKb6wOMgPp/YjhVI7quuxTB+xTIrpvb3PhPDXAw55z4WHh4fHyWAzQJ9PDBsBXJhY0mEF82u6X9eaFgIY1JM0+1td4H8mJJWnIJ4on150XtiW90431s7lrumPi+enTQNgUcseZrlVO4eOW+vN4uXh4XES2QLgamJqTuQjsWvtyppc6LxugOrBfD/yptBOpFDfb1jV4I3IGcIT5dOLzutbLQJWjkyglJ767yGNIorSCea1N8dXOLNCQ8c9MZGJ9PDwOL3puGRJN/mZG9kV1ToBJMxzzGmlABf25af8bQq0bgK+MDGpPDXxRPn0omOGVcLFuzYiVH7MsuE68orta9GV+pkP4w1Dxz0xYSn08PA4U1gL8ECtsQ9Ag/DkDPpFnTlmZ2tRKHdV9/VXnsnzXB+Jkx7o5fGa0h4hwLndmZ3R9Y/P6Y4WU9/XpYWtbMel1lm/BP6V/Ixqz01wOj08PE5/1gFvebTKzP3rDgsBnN/jcG2bowAhEI/XrV7ZPcFpPOXwLOXTi2aAN9rzm8pSg01zOw4QtrISWDVdVi0fOuYlrz/Zw8PjNeBFgB6/WL61SGsDuLbNZt6gHJ5u9oxdM/nl8ET59KIZoEiFaoClwDuBs2655ZaHyM87Dt5QKA8Pj9eGJ4BdCFH6+cXB2pwGCwYlGsSAbuDPE5u8UxPPfX160TT0Pu3D2csG6lav/CNAy81PC4YWBgEem5CUeXh4nFF0XLLEqX58003AXzuCmvaLab7ej+21yoa+/ljd6pXe1LNHwLOUTy+ah95jQHHB/oVANZDG60/28PB4jei4ZMka4Frgmz+d6Z9Dvi5aVrd6pWclHwXPUj6NqFu9Mt1y89NdQCUwHVg/9NVVQ+9P1K1emZuQxHl4eJyRdFyy5EHgQQAuofflj/bwLOXTjx1D72cV7Ltm6P3B1zgtHh4eHh7HgSfKpx/rht6XA7Tc/HQJcNHQPm/9ZA8PD49TGE+UTz+GRfmcofc3ATqwtW71yn0TkyQPDw8Pj2PBE+XTj7VD72e33Px0GPjQ0Oc/TFB6PDw8PDyOEU+UTz/2kh8a5SM/g9cbAYk3UN/Dw8PjlMcT5dOMutUrFfCnoY+3DL3/qW71ypaJSZGHh4eHx7HiifLpyc+BwqFP/z5RCfHw8PDwOHY8UT4NqVu9cjv5KTYfAa6rW71y08SmyMPDw8PjWBBqaIk/Dw8PDw8Pj4nFs5Q9PDw8PDxOETxR9vDw8PDwOEXwRNnDw8PDw+MUwRNlDw8PDw+PUwRPlD08PDw8PE4RPFH28PDw8PA4RfBE2cPDw8PD4xTBE2UPDw8PD49TBE+UPTw8PDw8ThE8Ufbw8PDw8DhF8ETZw8PDw8PjFMETZQ8PDw8Pj1MET5Q9PDw8PDxOETxR9vDw8PDwOEXwRNnDw8PDw+MUwRNlDw8PDw+PUwRPlD08PDw8PE4RPFH28PDw8PA4RfBE2cPDw8PD4xTBE2UPDw8PD49TBE+UPTw8PDw8ThE8Ufbw8PDw8DhFMMbrRN//ws0dc8XBsm43pAYM027xT/X5o+lkVfyJh5v7/6Zmqnhu12XxkvB2vX2rPal3SfHFzTfPeegXISBUt3rlC+OVjjORlpufrv1rfM8/D9YO/EvJwKBTI31xN6Si9yeM/rf3dG1JV4arDoamZC/KlP67bFgzPbF8xWV/qt5H0Frw5LyB+isG+ge/malOvanbLl9odQ888Z71t3eWvvHTHf+5uGxmya7Mhmldjv5fxZk9eqTxncFJv1679e82vtA4d95ywPnM22+KOGgXPPDvH/yPic6HiaTl5qen/K+bfNfkns0fdIrt+pDTpjfbi3aUpJurW+vmZDJ2IttVmW2faUazs+IbyrcMXlBbGZi2obFvY/9l2i8r/zx11RMf6ztbVaE1XEsiADz1DLGlwL7N2v7l96rgefepwP80r77uQMvNT8eAy4D761avzE7slZ86VD++aeayPdk35Tr2DxwwtE+/pSv0xZdqwpm0Nvi5VOSn/0Xbpy4JhYyZlzb9+k/3XfGuaNAtu/OjG7ddX3veT33+WMdvL7t0r3XI+QRwNbCt45IlBybmql4f3Prxx8wDldu/pou+vzMyQf9AyaBKJac0Tc7uqNpfXTqgHwwUnaXt3Juuyd1nUfJev2b8VW9bcnc4U/OGJSE902bJjb+e8n8X7izeKzIHP7BfWpV/bF59nRw6dwy4FHjgkz++9LQv7+Miyt/97Bf3Vvkaq64wngATfsebza+84UqA4rPXT3pnY7aWd/O+NwSqA1wfeNONAPf0TXv78O9bbn66om71yp7xSMuZSFbJPb8vNYKXyDhtEeGb4UypfNbcSXGJDD4VurK21LeRpLaftUJbc+0572e/cri7vQbghmcQvKSFr5meivIWklDkv+aChR+mXg/zla1Z/hp3AChy6R9IzitxBhfzpY8vmPK38BLAVrcGgGu+ePvA/f/+oR9PVB5MNP3I5tt1CVULaQ68B4Drcmct2VZ8EUhAr4Je5j0FwBTwATJdR/EcHuLfoY9rmklzDSYAIdgJzAFIy1ruIwPwT4AAfg9cBdwKfOq1vM5TnN3rZwbw74sgFDwUNe6LJzJg6/iSq662LIeU5XDP0rf9TV9ZNXU99gerl/76Qn+sA2A28MVDznctsGZoW7yWF/I65LPPO6HP3dg3H4C/qiwfTQTOhun8uWOg9r2dt2ND7c98H1050+zhPPPAAtPf9YVgpoYNaZfzi1x2V65FA/xV95A5+KEPAHcMnfsPwJXAD4BPT8TFvZaMi/u6KVo05W+MJ0Y+72DmyHaoxAXgXuwxv3HjkcKP1eORjjOVjCA4SRsAQBOKZq0bgLCwuQ+bpJYGIKHn96/HGfP7CkPQjhz57AsVHfYfRVIrAXCScwnl1OxDv88q87pxuZjXKZ2ow/Yt0JqO6xwdKB4Yek7SQ4IMsA730EOvGnr/6HH9wRmCGLoVcccBO1+urWxw5PtBJ1/3tJSbFwbL9w3vfv8RTnXFyUvlacffHpBVIx8q5GgbptoZdUAElMNUvQ8ATflG9leKwMi2EdkNUFifXDn0/pHxTPCpyriIsniZVuTwFxrgjBHmMUb64TWaxzFjM/YGvFJmHul7WbDtHOF7VbClu4ffbzH2FB6AOgHj6pXuzbF/5XFUhm+LGpPbR8pLL+bm2BGiIDt1NVr2tYJS7ZNHqDyOzJHuxxlR3k9+oRsq+AKwGW0xaUovPGrMB4/jI3dYVT76eewDcOTH4dBfF9plI/XX8IZQGJIIh6HOiAfmaAxfvCioN6Q6/sfrSKL8Mo2sMzrPCxnq/z0uxNiMPVJeei7r46DQzDIK8tYvR2sUn5KIkRI9mr1KHVbKPVF+Vbxc0VWjh1gU9NHLMTocwOOEyTG2mVpYvAtv8PBtOlLJLvRh2AX3c/j3o6dX6JIYwCFt3sN8rGcS1lCu+wtyUo6TpayOLstnRCV1jPgP3/Xy+S/GZrdnKb86lFmQm76CL0Jy1Pfmk0euJo6w1xPlV4M6pPS74vCHQQC2yI1+djxRHi+yL2Mpj0Uc8VtxyDncgm1t6FaOtoJHRTlnjD564gwW5Zabnx4p2UFGy/iJuK+PsyY6IyqpYyR0zEcO3ZZjsJQ9UT52hFHgsvYXbAfGWMrO6FNRcIw8vMryRPnVMTZHHU0/7Kt8n3KBpeyOEeUgHieMBQUuoTFl/YiyUHi3XBQaokBKwCn40fBdMgosZW1YlHXzxBN9ehEYbtQEC7poTHGk3vmX55Xc11Nvvrfwlp6xDaEjcCJ1yDFbyifiHj/TKKwNgoWirEa9R37pjtRVoqB2OkZL+YyIPRonUR5bXh19VHDVkNUsALdAlIU75jeepXyC5K20sWX1ZdydwNjSbgG6GGspF/5aH7pNeoF5oQ/1Ked0X8FvxJl8DwMjlnKBN8h/yIiDY+EYzAPf0b86ozkOUR6qk47PUvYd4XuPAgr7kQOycNstOGZ0WxTEXHiW8ijjIsrykDbkGEu5YNMpdF+7Y/76TK7QXy2+LGObRW5B2dXGSGz+KFmwz0INifIosuDWDG8eYilHYaylLM9sb0dwuGEUKLCUC7ePlWMI9Aoe/aszmny+yGOvtw+pto6Ul4WVlFdHvSwKg0LreHTbV9inrAoiUQqCfY/RUj4jPEPjIsqOGHsaRy/ogdTy3wlAFlT92tjs9Qr8iRPIHdJ76RSUXf0IkdiF9psFmGJsBHehE0Mf8nSM3lGBLgkDZD1LeZjgkfqUx8tSPsQJXpjPZ4TlcIzk+5SPpdoeHhFSMGBAqSPmZWFA8Zlcvl8Rl5woDPQKFgZ9FYiyqZxR9/XLW8pHGpFzRpT3cRFlm7GRXYWWshqKFNIAWVBhHeI68gr8iRPIMbZP2RGjZXeMMAy1Xgv7jy3AJ8QYS3lMoNfQ+0gQh9JHRLnQUvZEeahPWYxax35x/JayfQSDrbDBZHqifDTylrJdmCVHcSS4+f2GctQrHFzolTiTy/crYplp3xj3dYGlbKpRUTbk6JgEMcZSPiz7/QC3fvyxQm05I8r7uIiyVXAaxSF9ygWWsiqQA21s08gr8CdOIIdCLyivhZayTxSYDkMtocJKPofCPKRPeaylPPQ+vEOa6DJvlRT2KUvEEYaknDEUBHq9Oku5sHE0HBtQuK8EES34eEZUUsfI4aJ8FE0WQ3WPXxU0T5U40tGFEd1eHfUyOIZlHs19XSjKx2EpD+f3GRdDMT6WcoH72kUbYynLQlEWniifBAI5wCgor3ahKBdsD3voCgX4ldzXh/YpK2WMiHLWGOO+PpNFecR9XdiPfCKiXPhUDJ+p8N6UIYoLDjkj+tiOkRCAZo9xSR8ZOdz/X+gzEkdaB8AT5WPENnN+c4ylPLptjBFl9yiW8mEM53fhPfBE+VgpbJzamEcU5fwhow+BPnYCqDM5SOjVEsihMApc1m7BdqhgWI487C7kK3zjEPd1YR/msKU8UmMpE13m79chgV5ncnTqkd3XJyDKhQzfp8J7E0YUTkzuzYQ3Sr4OcUbL/lHHMA2Jsk9lCwcPHqkOKhQEr456GRzdNgtbNXpB7muF7mvloA/VT4WWsntYC8o9kiiP26qGpzLjNPd1oZVmjHFfu0MCnR9LW2gpjxFlrxV64gSyMMZ9XUhQvLKlfOg+p8CTN1xAhueyVdJAlyoAh7ivlTiTBy0fOdBLvDpRtkbc1wVzB48VZe+5GWXIfX0MAelDXcm6Xjh3nTpSXnqW8jHiao5ROHnIGArGKevKLfDqjcqPdaitrDnDeX/G3YNxaXloBWPPbIwxlrI7JNA5FKLAfa2PvQlnRGafJPKW8lFEOVBg90oxLMqjWEfotyy8M4dbygaGy5Aojwn0OpNF+YiTh/hPYEhUIaPu61F08hO3DP/vq/qD04sggHBe2cM53KdsaFahihyp++WME4QTxdVd82gVgKIw0MsdqUsK3de5QybaEcIe9kyccfdgXCzlsaJsjhkS5Wj5bQvQCqp+XXmiPE4EcjDiEjoUf4GlPOq+fgVLueD32pAbaiSyMt+n7IdD3ddntCiP9im/yslDCjmSpaxBYaBXoOXmp72ZpvLkK+9jEGVGRLng/gjlifKrQApXN47mpCiwlA2cka62Qvf1YZbyUUT5kGjs05JxEWX9ZSxlu0CU9TF9ymNaRl6BP3Fe1lI2efk+5eGK/6jTbB4Sfa2UieHm+48Pib4+I/p7jsJon/KrDPQq5EgNJjFWlAVjZzc8kxmylI/Bfe0OLx5SWOo9UX41uEJqR3dfj9ZB+pgZvQp04lCjQnOO1Kd8RpT3calIdXmopVwoyiageLf+GBcYj43+sWcpjxfBQ6OvCymcf9lFYhlJso5kOAzGIj/D18GC31sFfcpdxTrZXomuJJMH2yixe6js06qenrSAO+dcNnKcRHtNRPnbN74pTP7BHLzpzjWnSjTmEfuUAycwTrmQ4QZTsmDfuUq/qTCC6U0XhT/X8fgmjfytzAL7gINDn5s7LllS+PPTmbxl5R5SJBSHLDauqEp2MKNhI3PT22iRVSREGCvs1//t/p9c02ZMagD6sldNsoCR/nvhJosW3rFQa1jVcKqUuVOGWz/+mJDV8qgVgFKjRTAfiW0ipI7hhEf27zN6xvwmRKZ8y2duvK/Od/41HdYcnNHehQCM7Re69eOPLQFuAOqBDLAH2Ays/eSPL029ikubEMalIvUpe6Tgt4sKDpZUjnxnaQYf0f/KF83fjPmNZiRoOueLBOMzcJORy3d+9oP/WfmcXqv3UCEQDtACbAEenLejcc94pPM0JZBDoh9xmCUEtf4RO0sKRdukm9H3f4nhBmgGxX+QZaDAGiucNnXSgMuAo4gW+ZlRMpeedJZVb15kJA/5O2kEQt//z19ZsZxjNxm9rR307S/u79sUi6fX4fNXSsOnlGHudUORdWh6zy233KIe+8R3ZgaCRf9iGsGzDWEaujDaiih6QUdfD7xUt3plHODbN74pBPwr8AFg2tBf5r5945sagReAJ4DHb7pzTdeJZ+OrIpC3lBXztAMoJVD4j8tS1pBERQ4DF438ZDBtuDygxhoGrhgbJfyWR9d8tbptLwOBCPfNvoCmaA1SCTA1VMig5q4XDwipXhBp50mtO3uv3pM70Lz6utNuek4hVUQJ0Htz+CVMd3TqHY3FloElFCkkwWwjbm4tutsP+/O/66F05Bxv5Z77OvxVdPkraPtrDQcrppCcUYEvIigZ/POPDSfwvUW/WLRbCfUS8BKwEdjUsKohd6Q0nUH4XSExD60TnA7s9MMod1Rwo2Swc7VEB+aMObYh3Dj2x0E3OF97+JpF4QewMHg8eA6/r7qOnVV1P7/rsf/74HW33ZkQWtEsI3jJfwRz+ltSMklWCDTdj98oRveVIIRm3/rxx54DngHagV3Apk/++NLuk5EJ44U4wuLSx8W3b3yT5s6pdj+r/YEWvZIb5/43e8vrR76vyvaw8YV3oAnF7c41DKoQ/2zexTZRT8fKDB2pSpoHJ+NInYCeIywGKY33U78vQaQZRBpEhiYjLh7Q0qIBkBnTsPvCAacvEmhvL442ObrWcdOda84Ui2AMLTc//fF/ou9HZwU2HtPxn1c/IQO81/o8W9X0kf2ldpy5mXZ0u5e5uV4W+muYGjmLXlXO47bLPsNlo98hNdThEVGwUppETI3f1ugEi/0s9Ofd2QvjDrYd5zczivE7LlnDJJZS1AxalKZSmNagumDAVVdnqzXzZbzecdGX6tP7Gh/n+dlNgb4Yuo4rJDYWUmbRLYto0qWyL0AsbaIrsQV4HHgKeO6mO9d0nFiuHh/7b37qa+8g+aX52iZ+ZvyOXvuLuKqYtHEbVzgfYnT08ZHX7Jqnd3G20Tp2opchMspgu1PNVrcKhcbbRZoFWg9XumfRj+TeA8/SZad4tG4RA/7IK6bVJ1Blmj5YIUTTLEfdsbDr0V/2phqTN9255tWZ9RPM3HvWP1C8s/eqBTueZG6qGUOvxgici9DLQaWxUmtQTuvQ0SaaMQlNL8KnWUjRRdYeBHtsI0oi2BGZzY76xVhVZfg0B5/MUZnpp3qwl/J4nJKBfiLJhGXYmfjBkvBgomSa6xjhuMS/V3eK7qnNTn/ekHR98seXZl77XHltuPXjjxXvqXqmP9BzPnVDq/85uc046ccBiS5MgnqElDOAMGdgRq5DoJE246A5KCNFT2QfaT1BMNFLwPLjkwGEkmhSx69NIhawqA5sxzJzPB1eSp8VZdq+/bSIGNmpJqVmHFNpZGSIPjtKYDBLNAuGU4qj19NjmCS1/HQ8Bgz4FLt1xAYBj2WFuu/P/3N1YiLzsJBXLcr3f+i97xa1bb95g7GdC5f+hs5IvuV5YdNmnp22GJ9r8ek9v+XBovPY469jkspyeeJRKge6WZO4ApHqokrESasA/UToV1H6iaBpkspQNzXhTqrCXVQEe5ip7+fcVBPFCYdATqLbgCWQlkHONlXGMqyMZfSnLL2p3w5s2G8XP9GrRRqBVmDgpjvXjFxs49x5Ajirc5L/pvaqmrdk9UBISSHyCy5IR1Nu0rSzTZqVe8p2tPtTPn9DMuDrOYVcpgDs+dyT/7LG3PjtuBE/6jGLZB9btPx9+Vt7gHICKHGAH8lS/te9jGUDWzm3/yUOHZuv0Hmm4jI2RWaM7JukBG8WPq7UTDZU+fjPOX4G/YeHJvhsyYyODPWdCS7qtZlupBkIuvT5NKTmo8gJsKI/wqCj6NDiOGYS03AIohETJlEZRnNCaNLHAVx+rnXxfEySCESQug80iaYsdJnGdJP43QH8Mo5PdeIXcSLZQcqS2aZf/OcT0w9L3Djzk889/If12c53fNHw0+H3s1lvplaWsNgtYYtxGyv0DfwxMIlbfUtI56bgDC4mv3Cd4g3mPmbofQDYSsNCR6r8MiIB4YwIdYXs56Lcw/g0l6we5A75bv4kzxqTjloluFiZhBXskZImJF366D1NCw5rF5SQZYYYoI4M1cpyYk4mpdmpFifXtyGXjj+vcoltyvQNmtFAZGakp6jKn3CLjXSqhIG+Igb7TdxBIMUtAxNmfVc/vklU7R9MvunRX4RKrbHOEmH40JSD6+a9SbOKNQL+2bQ7ZzPg1o4cF9U7qdZfQHObaM8ZdGU1sEcDUxNGCR2ByXQEqoj7YoQ1g3NUgBman2LNoFKYTMKHOGQt+UEl6RSSVk3SqVkMiIyTEukBR9nbqlof2VSRap0qAnUzE77qWNYIYmtmytKNHts091iGb21Xke+FdWct2DEQjaU7LllySno4bv34YzWbQxvaFqeXglLUaS3UmlDiryYnHcJGFE0IOkyL3WKQ0mSQvb6ttPm78IljXwb7UJRjozJJcNPktCxJX5KkP47mS1MmoBKXKmEh3DLa3elkVDEOfhwMssokqXyk8JGSPnIyKB3pTyHNVk2aG5T0veAIbUObIbdOskVFv65uy2nZFS6EYEgnhKs04UqhORaG6lWmu9vStWcdtIcYKN3QvPq6E3KdH7cof+PLn0660eiIg19DGGEZ4aFJZ7Ghth5Nunym8U4OJMv57YorX/F8unQJ5bJITUOJfJ1hSBfDlQilSPmDZA0TJQQBx6Iy10/M7sdw46CSCJUCmUFXFobKYUgHn2vjUxLTlejkg5S0oThiTQk0RyMZCdMZm0zCX0JOD+EIE4VAR6IrB0PZmMrClDbG8Mt10KWL7roYrovuuOhSYriguRJdCjQXJWQ+sjA0kJVf+8K3xrWv9cs3f6LdKC8pH81/n5ExQe9XY9YnHaYrUkxRpI8Xoks5WFpJkaOxcFBjZbfLDV0v8PDg/bR15y1c4a9hs6+UuFnM1HQzk7OtWJrBn6evxKyfiVPqIxtwSZk+Uv6KkWU5g7kkdamtlGrNKAO2BS4iZYxWeJrTRyD1LKHcNiJuCwEVZ5LpsjykmOl3CepHb+f0usXcnvkIDf5zkL5jyEqlCNgWQcuiIh7nqfe9edyjNb/2rS+MMakq3QpjRWIWPtfHGt860kN9yYucKcwxnuertU+w3T8aR2QPnoWWnsy56XKmihRSCV5y6tnhVgKCMgS9KKqdLj4u17I/Uo7SNISVI9DWhJFJUhMcpDdUTxMrKSVGJNcFuX24ThegiE6WRKf1ESwdIJEw6Gzzker2IbNafppDAZYQ2EJDdwWhrCRgS/y2yj83cjQKVBDGb9Sj+yrQjQhC8yE0DaFp+QdWKNDy3SMuEtsQ2JrC1RUlkeej7/z6nePqxfr66s9ayjBG7quL0Pr0ci188CClXe2Y/jnkjBfwDWjIoQjfkN+maFmMbSVz2BqaSb8Zw+cotFwEfzZGuRSUSDCFoCcgiPsUFi5BK4ffymA6OUzHRncdTFdSQxF1TpSYa+JToA1Vo67Mz+KsCQ1NHLnoDe8NpdN09jfSlt1DSnbhC+TQ/RLNpxA6YGigCZTQYCh4Vojhdd8ErtBxNAOFhhKGUggQOhINhAZo5HrT/V/+z5+UjWf+f+Or/5JxQ4HCh1EUtxfrU8Q0ztJK8Wlj5xHaEdX4wuIgB8L5e2G4DpP7OpnW3cbMng6iyQxWqguMCDJQgqu5ZAwLV3dJ+otoqZhCzjBAKXKmj4wvyEA4xmAojEJQlhqkLDlALJsilMvicx0cXcfRdFyh4XdtKgf7KcqkDp20agiFEBJNcxEivy1wQUkUCt3MUhaJEwvYBHw2pi7RhmpboXSUNFCujpQ6cujdkTq21OnoKFb/+LX/OK6A6uMWjI4Zk0O/r7/iqBXd9esO4Gu6GmPySyP7opkMMzv2U5SJ4+o+OotK6YmUEA9FcTWdRDB8tNONIWv4OGBUAVXHm+zXkpG8eePgi+M+45JRXlJ+67J3HfG+Vfb3UZodZGpvB/N6Ezw0cwbbJo01FJNAawQeqIW2XRcw8/k9wH5qjWk8P7+KtSVPIYxBmrQUV22spnPqcnoWrCDtO9w1GlBpLuFR/sb3KwxfYZT3H2lUC3hWnccGsYKEUUq66M2keTM9gFAuLaRpIIUPCw0XicBVOq4wcNFxMHExSBrRkXhjn8xRmhnAbzkgBa6mYRsGOdMkY/jJGX4QgqzPT9bnp0iLj1Ouj+VHS99uSDH21tbKVm7Yex+qrXhkX4O+n4rUSrrT6wmZOWZaFlsCfmKhvbxxoI6wSOGieNKZwgE3H4dRaQ8yK7Wb+ckGAnaGXiAQ7CM7aTrK5yczZQ5mvJuWvi58qV4WTPoNocoMmiHJDfgIiRKiszsxSkevPVoBtUPFQNk+sEIo14cxWIGZKEdYQYQycIVGTzBEVyBMd8BPr89P3PCRFiGyMkhGmGSEQU7XsTQdW+jYmo4jDGwtf98K80VXDn958hfjHmhz37LzzH3GERwgS8DvZtFti1C6irLEE4Tlhfgporl8Ku3RosN/84qEgJJXl+CjEiYkQ5QwjaBIo+MiULjoI8+AfcjLxcDG5NDyx1EmMPvUujtjR9r/atiyeHbgidh5Y/bpyuEcXuSq3nuZsukSzJSPrTJAtrScH58bwhpqQ5mOjW2Y7KuYxL6KSazPSa7qcCjLuJzd7zJ3MMcvq5/jj1M0Av7F9ASnHSkJY+iNFNEbObZ7G3KTBMlg4CCEQgmQ6DgM1zsGbn7gFm5B11pEJQiRwsRGQ6IQSDScoWPzL3Nke/j+vDv6wLFm6wjHLco+x6VE9Y3ZlyGAi87F6UeY1xUEllDRM4mZHZ34cxHetDaHz60EKgt+5eKKfgbCikRQoUsHgY1lZEkEE2RNG6U7FOey+F0HTUkcH5hVA4iSDG5IkRYh0oTIESCHHwvfSEEezmSXfMsx/9KHMlMQUDlqMx3EUmlCWYHP1hBK4AiwdYmlg2NouLrIt7p0HVcban0NtcAcka/IXKEjhZZ/IfL/IwRFmSzjjZByTP4rBHGRrzS6SkrpopQdNVMpLApmZgsL+kNM6e0nqedoK55EY+00fjTTxxc7z6f0YCPfX5Fkf/2VKG0F5fZ2ppgZHpqymE5RA0CVamclT1BJJ2FS1NhNZOQAOZUPdcy6JgmqyfkqmKV3sEDbygKxFZtfsEEtZz3L2cssuqhCCp0kUZJjRvdw1HkR6+QBbhS/YrHYiB6SYwdJFOCo/HkTw+d2YsCbTyyjX4YS1Z+3RoaIU0ybNonfzXwzb7Ifo7qhglz4AFY0xtpgG5dvncOz1Wfx79ovaA518ajzDgJCg5L97CtupNrMsnxwClMHkgT7bVRUkHX9GMUOFUVpcsWKtVUZFthNCGWjTXEpLWslGu1FAX2UITGZRBfQxf9n77zj46iu/v3cadulVa+2JffeMVUYTO8hJJCQEFJ/KQ5pTnEqSnkTvS8hCSEkkF4JhhCa6R0DxsbG3ZLlJlu9S9t3p9zfHyvbMjYBgsDGnufzkXc9Ozt75+7s/c4595xzM+i8ZJ/Ci5kz2KOOocpu4lJxN2O9jQg9A3oGE51tOYXsIMxuxtHMGDopPWggeqs4KNTn5uon8BarqLyKoJ0gTz14DNLJ0EMRadULqpeE92R68k8+aB8jk6K0o4eqZDd+M42lqKQ1nbRukFE1TFVDCkEwncQw05iqQ0pXyGg60tBAF0hNIFVBGoMUPkyhYwsFKQTSyYboZRmyuMTwx+xftraewgB5JJQACd6YUfJ6CJkd5QSSfSPR/mX6RhCvaR40BqXxkBABVnEqDQVT+fi0v5Our6TP6uPuU67F1AQFg3u4ZPNmPGaGVL7DCyVzacurYMBjsGzMAcs6aAaIaxcjhSBGdo2E+d0WvrhFzE4RspL4nF4UO0p+dBBvXzexQD6RvBIiRSGiIYOkV8GnRjDUGBoWfRTQRDUp4SehBknw+rEXryYmDjNevQES6Te/JMBbnlO+4UMf3TJ74vSpouY3IOH6Ni9qLDuQ9/uiaI5FWWQc5ZHx5CfKyE0VEUoXoA7PUVMybC9cy5bSFfT6W0GAL6Uyf1s+p0XmEawMo1VvQi9bj6Id8ByaGS/peAFmIg+ZykVkQqiZEKoZQDENREbFyQjICGTGwTEdbFviWCbxIos+QxB5zYx3EFKiO+BxbAzHRpcmurBQhQnCBsXCURzHEUgLYVqIpBSiTwq1zVBlgwIP/79v33bPW+rg1+FrX775pPLTnllZnzcVvTHJnlQVmybPI+3xodgDBHt/hye1kUBSpTSukzYcCtLT2D3qSnYWV4KU5KTiRHyHv1Bz5QCXRv7F3LaXsJMGg+lyolYZmbgHpCSj63gzNrmRGGWtbZR3tDNQMJGWsRNJVcQQvl7sqI2eDOKnFKHn0eMRtBtp4rqKpSj73eCaIwg5OkFHJ2hrBB2NkK0xOaXiKd1CrPgVUqE92N6Bg78ny4tqBlDNIGomiMx4cUydZMLgnMV/etuLDSy9YXHHQ7MuKOnSKzFkmg/863cURfuxq+eQUiwM00EOtOPrb6d8tEZmqklZeSOK8ubDE1J4WMd8mhlDM6PZIScSUbJWQonVQb7TR5NWRVI59M7FY2bw2mlsRSWue7Nu0Vehywz5Vj+51iAhK47fShJQYvi0KD7ieGQKw0mh2xk020KzMqi2hWpaaFZ2SkczbTClIyxkn1VU8tVv39X75nv1jXP7uWeFJ0aT/eZHTmbHuA10aWGW8SFaGM3ETCPV5m5KfK2cqLyIGhe0t01Cts1nrFVO3BlktWcjTUUGeJIUe3YT8nVQ5EtSpGVXUAup2ccRwwGR0Ej1hGjpLaM3FSYlS3BEGFQPAg1FDomsbSOkhbRtKRzbcaSDLW3HAdOGpClln6PQnnHUHQld3xLRjT1xlbYMdl8XsjeB2rfj2998W+Ng7v7hrRP+PLGgcXteGQN6EF2mOafleR4rX4ilauTEu7ly7Uo06bChbCZzt+eTH7Uw7S00lHUSzS8k4y9kbdVEEkY2O3Zun8WlrSYn99gUZA4eox01RTK4l7Ypf8UMtGFZHnQ9fdjfk7A8GIkS9FgFqfQoYnYxGTuMbfsQjhfF1tFtDS3joMYTKKkkIpNAmkkcO4El00hN4eniXHp9xUjhQ5EaAgUhQQoHRzg4OEjFRgoLIUwUTHQylKY6nB985/o35TF969HXV140+bTCS+rts+4lmbed+wZ0no7qOFohvaU/AeCc1G+osl6iPaPQYwv6TAVpefBIFV29gI0Vl2ALm+rOTcxt2kB+ex/T4mVMGTWO1JQniRUfiCw2opXktp2Kv2s6ot+Pk+yHaBci2oGMdZJM9TAgY6SEFXUEWz2WfU9BNHmnLuWeKQ31B31rf/jYh8OjqiI3CCN9mS7MkCptRcW2dMWK+US6KceIrvJokZWG6azNGzR3UTv4qvXmjw4e/u58aZzZz+DuELsfq8TUdHryykCkMEU3uTFJ7nAnohIilhvijkuupd+fvUs37Axny0cYr26jRY5iMFqM7A0wZk8rObEeAj7o04v4D2X+3zR+aVBpF1LmhCmSOeRIP5adJmr2Ecn00WPFiTspRhth8r3leNUACIFJiqQSIUqCmJUmZkVJpPuJpftJZ/rBzCCFdOI+dVPtn+6bPWINfg3urL1aX1kSzmyYeCJblVlUdzfy/nseZGrxe9kU3kpcpMFxyGOAwskNlJbuBMCTysZNRNIGZsognVaIFqVQNYdSTeJIlXS0CCWTQ7dZwdOBebyYO5uEcvDaCELK/Tc2+ygc6OP0bS9S7rSxqmwe60bNOqjSHoBqWxiWRW46SkWmjdGZFiozHZTFBinujOFrS9lKe6bH1xtdbzv2YH84X/bl5KYHgrkDfbnh1raC4q1rp8zY2lpc1tpx5uwjmhZU97mzfnlxfcF1wZM/SrRiFb2jHicT7H7NmvCm6WFn0qBXJAlrDhW6JPwfbtBxQBkAvVWgdQqkDxIl0JerklQFqJJ8v4VXk0gJjmUgU35EIoSIhHH6C4lFS8xuq6whlaxYbpi5LwArbwgnB9TAtiqEU4ZUhbQCA06mqBNp9DfVXXRUjjeH486ffs+8fu5CzeMM0KEeCAwtifRx8YYX0B2bzcWjsJ0qygZ1JrVaaMO+mqJRr5Dyx8nYkxmXLGN8HGw9QibQQSp3N4rpxxOrwButAiQDFc/RV70cy3fAYldMP/7+SXgHxuONjsGIVaAMSGS8GyFUpGMiMwlMM0bcGqSPPtqNCM3+NL25QXy+UqtQLW4qpfD5qWbZQ3lOznqgCSixZeYLPWLTZ/OV1kBQxIU6NM2QxCPj+O2EDPZLp2C9X1bdkyM8LwK7Kutq/quI7hFJiZodPNkume3QOe3P7JHl/DB+BRO8FpPVDu5UPowibb6f+DlTYoOIdD9Kl84GMZXfj72Ulrzs/PCYnnZO3rGKsbv7OK1wCvaUVUTKn88GkUjwbjGwX1JJ7/ZTEEvhG5a+EPHqJHQ9bVj2Q+Fk+qcKrJ7SUP+uuaDfKr/81pKN086+d4ZIwb3PvJeqlh6kfZhUPOEFmS0XAuAJTCA0/f3EtSglE75H2NODGlMx60exva2K3mApr65oWx5TGMdYoo5KREbpTm0iI+NYwRxsXw6oCtg2IVOlWqkkn1wiIkGMJL12gm7TZret04WXvJhJhZRoisOgqtKk+zJdRnDzoBp8zFbECrI5yP0favnnL/LNgS+8Xj9I5DqBeBR4Ali5ZNnyxFvu3DfI8u9dvumZEz3T/+5fiiJtPvV4C5MjOczJTfK0Zx2dMkNx8S4mTX4RKWHi5ji7eqaSSkq2NAcAyRRrEGcwQYYUHQWSroIZJPI/x7PTA+wt1vYLb2XCYUGvxdiYw+SBDBN7IsQSe3k5fxWdVV5CBb2Myd3OE8qFPMU5DIj8Q9obTpmc3DXIaXtbmLxtJ3Q228lkX7tmxx8sHGj/uyrl+ikN9e+qNMNPfvmTma9vsXTf7I8iFJVEeDuR0pdwMn68PdOhPUZH8XLSJ+zFbxyafuZIsLsF3g5BatAL7R7CHUn83TbaAAg7uxKwCJbQMracJ2Y5tHv6sGSanLjN2E6PU96Tb3pSVbFY7rjmWGDs84rQNpEtZLHxWE6Luu9T3/mV/Z5Vi3/g+wKlsp31ygIK0r2ctWEduakEeqoKf2wUj830sqlQYfyqHXx+61byRk+iIlRCwEgTL9hMMncXtjFINLwb4ek/6DOkBDuZi6pkEN5sV6rpHHI6TiLYeQK+gXHITAK7dwd211YyPY10+Croy59MWlNIGx6igTCGz0eRGqRSBJIFmvqMTxEPAk8BDZV1NUc8wv0tizLAvR9ZIueMOZsdC78AisPX+Tkz5AauEX/mf/k2G8VcvDLBR/k9s6KbeaL9CpaPPZO0bqDaNsWRPtREjO+seZmK2Z1EKleAkv3ReNdrGA9qtJsVVA604ctkt6c1QcIwpG7bTwbT1g+A56c01B/xDj0SXPyPe/K+FPxGnwjZdDwwma62MFI1kHYfQivACIzDyClEylwMp4uSVx7Gn+xi7dSxzAl/jtzZtzMw+imMaIAJd/fTtq2UnMITsbxB2vJ8xIM5+LUQJXolYXnw/FdGmjR6H6d+ZyO2mSbXU8yJBeeT78lOYbSR4A8IniFNZe4eZhdtYpwj457mmT//TWrsv3pUGSZb5bMFaH2twhY3XnXxOcC3gNPIxkI4wHaywv0k8Pg7lZd8OH5Y++0rT81/ZtmS6d+iXVSwILWWq7dvpnjyo0gtjWOrKGr22t1TX0HfihyEPFBualRflBnNB9J5YoFy7j7vG/x9URHWUK3TuX0WVzX2smD5zchEB72lSaJnOvjGZnDysofamC7kAS6m0XMOljhQKC+QMRk7YDGlO83E9i6CnQ0MJJr32k7sL8jo75csW773Heust4llPxl30fI9Vy//1uqn8Yw9CyVUirRNnP7d9CWa8bdtRZESqUgyEySZKkks5IOYQnhHEr1ZoKVBGkEUIZDpA4aO8BWgBIpImlEsT8AOKt5NTt/OdWTiu4B64GWg+Xgdgx79zP+OsSe91JSZtZt16UWc4HkUj51G/f1k7pgyj7EDpxBKZW/w95RbzNCT1AyG0VCIlK6kY+qfkdrBzhZpqyR6xoNU8Pl3oeQMW2UwCjlP5hFqnIZwVGQqghPrQCZ6SXgLaSs/jbayU/EYfio9iuMTQgZU+nNVsV4X4nmyIvxSZV3NW6uF+zYwIqL856s/7pw9+mNi78yfkizdzP1czr+5kq+l/497Ot/PYCCH9oIS5u5ppCA2yBNTT8BRFLyZFCndYHTfXuo674YJa/d/MZ4GndADkkhXLopMkJ/IDmg9IYHiqOlw3LpJgZ9Paag/YgPx0cRdf7w4kV9V7/M/o/DgwOUEe3eiW2mogPEnpujeNAXRnmbWunUEEjH6ghCOQ+PJ55Fz9YOgOIx6+Rt4m/MRRgBxmGhrKSUdpmS3LvGE25mR9mGkstkWNg6WtPCIbNCGQ5ybPDbry1q4KG8548JN6IncRP+OM789sPPMXy6+ddF/Nc9141UXa2TjsaNLli0/arwhtbW1OdOdDYMPLDiZRwPnUin38BO+ijLMferYgv7tOTSvKDuobFo4nuKEXe10FhSxt7AYJzSBxjGL+NdpeSQNhVO6Lb6xeYAc6zn6Cx8gUxHBLmT/TEKLU8J9mRPYJGYR9cyCochPX7LdKe5b2VLZ2/OPEzd5k2qiL4wT7UGm64FVS5Ytb38Hu+gd4bM/WlQ/ZesZk8955Z8ASAV2nFvIdy/5JkYXfOjxezh58yvkRF7fieIIhe6iAhqKMwR95Zzou3DAGx73U6Fot+yrNudygDsWfyxRdMVzPqTA015Kurwd/0teHvWcQ9qyGR+Zy6xUNcXagXSx9tLniMz8IwCpgUriHdMwEwVkBssw04OUTLmb4Kj+/fl5no0C/8s6/uYqpBKmWw1im2lM3U88UMZA7gQS/hJKdSFHGerTpbr4oSLEs0eDBfxGGRFRvvnqK8zLR39JixSvpn32rxlw8vi88ltUx8ZSdQKpBGfXr8FSNR6efiKOoqI4No6icn70Ya4x/g5GNlJZ78gl559xjO0KPQEvRfHs9t4QvDRRZM7cJGv9GW6Z0lAfecsNP4b4x70Xfbo0p+FWJQol39Z5etZJJK1eBJLxnXEmdhy4d8noOoZpYhVKeq6zcYoczPbZTNv4xf3FD+JEaFaj9Go6/bKI3F6bqCN5pXwvZdN/xmm5caRl0FZ/MVO7FlBqlw4d3SGjbeWDJ0zhvMA/WKhk652nBiruVdTMVRdc9tK7unLUf+K73/vWtkv0xyd+4NSfkRAh3m/dzry1+cxLzCGgxljVE6InFUVJbmdPYR8Pn3oGX/377zhhWwNbqieQG4vz+KKPs2n0eNaPzUZtzhiw+fm2LXTP+T8cPeuySztZN0Gv7eF+8V5e1N6DHBY1XZB2tvR6lK8DDx+tBSfeLm75xehQW8f09g/vqQmw4XZwsvdt6VEO3dU5hPfGice9xPQQ/rSH3NAEwqEiFN1HMlTBH/IGafQvQ3r76ApDmRzFt1s+2V1uFv8vcGtlXc27rpbyO8Uzn7vpMWvhneeIog4Cu2cQr94EGah+eCmGb/JB+3baJg3+RkpqbkQoDj3bF9D1ylUI/DgiQjp3HZPnP4K/YADIinHuymmEghej5o+lw9HstowTV4UIRW0pHCCgQFgVe4t05be5qritsq6m59BWHv2MSP5DUsb7bccqCnbPQWZUwkY/Vexitzqek3ZtpDAaYWdRBZsrxoIQhGMxprdt50zfY0yuzOYz67FSAo/48T3VTMLwkvRKiuIpHODxOYKMLtdcslqePaWhfnAk2nysUZrT8AczHfyNHoqJ5AkOi1auorFyNLvyVPbmeymKePBbgpTXQ25kEKtIsvdbJl4PpGN+9qy6mn7bZpTHYqL3TprVLfxAfJELI3mEkhbdQvLCuAeZMWE5J4dspITnEjrbCrDOiX9dK047mDLEHyrPkjePvVx8gRudScpWRUocIfjiRe997ldHuo/eblTFeHhp4jMTv7z99/zPxC9zl3Y1d50IgYzNX1YFmB6yWeMoJNQFlNi9fPOuh5nVuB1HUcgJVLD4uo8T9R2Yw39PS4Yl9SnSFPKPraN4Ob8ZQ8BU1cO0onz+oX9lf8paUbydE/sCW7t8/k/c//75Lx2pPjjSLP7S3ii1uadtqpi5Nlx2g9LX18iLqedQGIVIz8ccU5otyARM9iqUeLNehY36Hr5ffRMODobUKTHHcH5sfNd7+87+XqEV/ktlXc3I5zceY1QHZ97+0qbHzylc1EGyYhdqu4Jd5tBVsgwZ/xgOkoRioppeNqcfZOxJryAUh2SvAd42VB2kpeENJph88oOoOVFEAvLuLiTf+yXU6kpSjmRzyn55T8ZeuPjWRcmWpStKgdlkV5zdXFlX8673nI6IpXzD1e9ZeXHZx04K6fk0Vn0VObGH5wbP5bbwpw/Zd2pLG6fsWs3ksasoq2gEILz7PMSyAXx719FUWEA4Okg4bdEXhNsuUDhxm/z5oo1yyfE6X/NG+dud5y4vL9x5kZXRKfyRjr/HYm9hEV0hFeGATQ4n7GkEJI3fziFU0Uu032DP8tFkzDEouefjtcMYIsZ0/2Osz5yDY4boDTpsmvBrrqjaSNGwqvNF7WbfjO2DQQHGDt8o+fnJ3xa7Q6V8m+vTFbR6gChw5VmLdr75DPp3IbW1tVdttkrvmCa38OC8MlryLtz/WnXU4ufrUpSkTZ6LpIlZWTe/YmdwPF3cdvYUunJ1vKbNvMhmPrhzNCf1a+zytLC08kaiRhpVKrwn1yKdu5C/8VEsYRDIxFncGM1c0Rr4jAp/fje56d5OrOvHfqg1+cu/q0ou63ufojG+Hn9gWpuiLigzCYoJHsFUX9YmecT3rLU1sKvnnPhppdOS41BQdgM/BP5aWVdzaESYy2FpWbqietDs2tVVcwNOTjfGmiCZ+TEwQf3DZBKlF2OMeZE9vU3kVkfJGxfFsQR9LSfQ98qHsS0vvvBuxpx2I8KfQe2ComWTyan+Ao7uY3faYUfauS8tuXLxrYuOWY/biIjyzz740V+eXnzWdaW+KjqcnzB4/jYig0X8tfObbC8vwFI0igdgUmcjlbHtTJ78PIVFe0EKiuo/TPqhJjxNq1k7dhyTW/cSTmboCcHPLlc4Z5386Ofu2vqXt36qxz4//el1pRPHv9weyOmms3cM03/QiW4eOnXbdl4VXNaIYwsa7hhLJK3zYPEF9GvVfCRjkpM6UAQoGjBJnfZLanIb9m+zHUFFW8qatiuqASS9yvoHJ81cvCO3/NxZrPuUgVkOdAAXnLVo5/q3+bSPGmpra2ekpbrx3+lpfLDkepaVVHLR9tE8NOsD9OVmC7yc3GPx6fZNDHTuINK+gKQ3xN/ODNFSqBNKZzh77z18qKGGKiNEs9HB4uofYyoWJRhcUxLlceNjPCEuAKCmy+R7m9Ob8kx5ZWVdTcN/atvxSMc3f/9dS076QdpOsLz5NiyZAQTVoXksKMwuOxon1e/HExZZ8zlOdjWy37li/OZpWbpCAHubPC9XZk67DWIWxl5BeobEyWiYKZXe+hwQUL6gG+nAwK7TSQ+OQgv0kCtb0cdvBh30PYLSh8/BU3UVe01oSDlWRrIU+NniWxcd0zeeIyLKv/jQV983N1x919jQTAZb7qDj448gJWx4fAnegclYWpxIbj14B5g69RnCeZ0IR6Ns46fpf6kVo+ExVkyeLOfs3ilKokkG/fCDDyicu05+/0v/3Fr71k/z+OFnP/vsypmzHj9JCElPdBQD91dQvasHXyJJSiSJTFcIvq8fzbDpWFPItvp8Hl/QwYAzjmTztZT62zm5sI/pe/KYam2kuPg59s6NYyvZYSttawy+Mm77B5MvTABoLvfSOC6w3yU4xB7grLMW7dx5RDrhCFFbW+tFkmhxckRH7gvsKWoknDL47h0F3PT+a1g7Zcb+fc96+QUueOFp/vG+K1lXORG/naR8fT2n95TxOYJY2Hyhuo5OvZcz/XBSUYLfi+tYI04CKflcY4Jrm5w/qHBdZV3NMZtq81ZoWbpCA7sB1HF96Q6e67iLYn81JxVeiHJo4ZR/A0sq62qa3vmWHju0LF2xzHScK+OjnqNz2p/J+6lG7L025tiszqQGDDy5GV6jLDgAns0axa9cS1feqexI28QdGoFrFt+6aPVrv+vYYWRE+SPfnjDFG2ycln8a8eYX2HbZMgKFEbY1nEpX53gQDkLYzJzxKDnhXoTlpWL9F+jfkUJb+TteqR5NZW8/1b0DpHT4/tUqc3c4L37vD1tPHYFzPK6ora29Lj+/+ZeTpjzvaKqlRGQOz4uFRJ0g89OrnHG+XQpAtN3PlocncMeYGuSEvyGESb5vFIN519ChTaE81cUzaz5Cjp0k7lPYU+mnLRTE3lTafp65vgzAFtz0zGkFMYR4PzAGaAb+AfzirEU7B45YJxxBamtrdwHVz5Q+Q0IfJKlZ/OlvVQRadnDvJefx57M/wqBxsCAo0qZ03S4qBlL8yhyNisI/Ch/nhepe8sOl7BQTaGE0SeFHdRy+vSlmXdohPlxZV7PsyJzlu4eWpStOJrvetnGYl1PAj4FllXU1je9ku45VWpau+CLwi6hjOj0131WMZ7sIPaLSe6Ukfap1kBg7lgHdfnx9EdQ+EBmBv28ue/gIe+X+dL5bgS8vvnXRcTOnPyKBXtJJ7EwMFfMQ/kIym/0EzohQVr6Nrq5qpJRMGfcMOeFepOljzJqlpHp8OK/UsXFUCcGkRXXvAAA3X6pQEJXJC9fIC1/7E13+Aw/09Y365fp1FzB37oPbc5TIhAt5IJtS4MsmFjzNWSwLfRB1XD/JtCeqBK5Y6bFWn9uYvwSpZl3XMfNlHh/v5ZJdKQJJh6nbY0wlBnSUDX3Oz1TJV886a5cEvnNEzvTopAGoDpm5xLQkYLFpcoCTWuA9Dz/GQt8c7qmawt+rdLzpFKm0irZlADOZ4XsiiIrCs3nt3Db7XBLqwWlp4USM6zdn0jX9+qWVdTWPHZGze5dRWVezsmXpig8Ay4DhlXCeBr5UWVez8ci07JjleQBFqnbh9vcrHafeTOgRlcLbFborIT1akOicTHTPPEY/tZGilgZAQyudhT35PTwnijOmxCBbL/3LwG+OdXf1qxkRSxngHx/6pFw46loyyR76Xvoe3T8yMVSTVS3zmKw3klsSxXEUxqz9Gp7eCWze8is6xCBC8dknNu5QFeCfCxWemin41p32tRc+W//XEWnYcUhtbe2TwCJFsW489bR/vpjEd2ULo8Y3WeN8GwYXTNgQmqo7Hg1MB31T/0vm3IJrkM5LCKVANdujof6/3D1dbrx+77avV+u29fDF9su+z2v3mUGSmhCsBX5E7eB9R/o8j0Zqa2t/Cixp9nWzuuQ5EDAjVsb1f9dw+nejlc/Dt+DTpNMDnH9WHtFAEN+eKLc07WFmagxtXsnVp4SI6YJiq5vR6T1mqFnoefE41/SNSY1y8s+srKs5bqOr/1talq6YAXwYeAxY6+YZvz20LF2hkC1NOcqRksHRT5B8+W5CLzkki3y8suBL2AP5zNvys3RgoN2DouGdcy2J0vny2biTluAFNgIfXHzroq1H9GSOEG9qncf/RMLK1pzXPHkEIg49L1QAcGLlWnJLopiWRtm6T+Pvn8LWvufolP0IockZTU2qArwwWXDPyYL3P+9sru7kbyPVruOUmwAcR/vSiueu6b140eYPdDx34me9LxaWnbhll37V2idXiWimD13BnJV3IlKuRigFwCu2Xja64ao7PrZm/Q3TupJF97Vmyny32Zc+ekGmLiQEPmoHT3AF+T+yEaA8Xbi/uEe9vxvtxE+g5FZidWzAScfweMJ8cmMHSMk3BzVmpsYQVyVfnRMA2+Yjqb9b1ye/s+qEl1uUSZ3NXN5fJEc5+Ve5gvzfUVlXs6myruYblXU1T7qC/PZRWVfjAP8CSDp05DWfgxL8MZaq4etOMvepm+2alV+PBAbaPVLV8Z30eczyBTwbd8SQID8DLDxeBRlGUpTtiGNLC0VREb485i1rxfuQLpVtKvGXcql46HOEe0+kJ9VKU89Kkh6dosEYwYxF1Cv43QUK05skizbIa93Up7fMA8DtgAo8UFtb+2uy7ro8YGWumVwk4na1iJgWqiIQIg/Yju1c6H20dVTV0gf/CjwE5JJ1R13x/E8+mqZ28IguOvAuYQOAx9aQdnZ2yFIsVufvwX/6UpQpl7M7nk21/1CkhIef6uLCdgtLwJI5fpq9whrfZi/yrwoa29deqkqBWmkXMNmu+ExlXc39R+60XFzeMA8A+BV0KSWlwXx6p7xviyPAl4ypwpE5GW8OgZpvoBRNYVXCYmjA/wVwzuJbFw0csZYfBYyYKNuqyMTN7GCzZtxJCCnIXy5E6U0qU5+dR553LpZjsrrrARIejeKYZY/p6RcAfztLkDQE73/eeXzG1vpXRqpNxyu1tbUS+CTZmtAh4LNAgOxCDefV1tYm2j54csRY1/s5bXM/2tYBPE+1m94n2reRtfSuGTrUL4Fzm+oucqsYvXEaAMsSNsX9B9bzvT/nSYRqMFhezZrIv2lJZwvSFVnZJRbvKN3JhJbMc8UDdskjnzjxaZ80vpYS5nxVKsyzxv5rVN3pvz0SJ+Pi8l/wAhAXQhREbPkUwJhxZ07bMfPif2wZM91umv1Zcs/5CWp4NI1ph6jNOuCMxbcu+vLiWxcdNaVzjxQjNqf8i6subj+p+PLSysBEbk+1EN75R0LpDGlPOQsnfwJd9bCu90kaI2soisQp74tSMRinuUDhq58SnLlB8pmHnTlTGurXj0iDXKitrdXICvIsYA3wx9ra2oOS7quWPngz8Plhm0zgfuCGprqLVr1TbT2WqK2t3QxMS1ljWD7ulwhhgYAzBk/gA+1n8Rfzt6yfFOfc2ClYwqI4k8953Wd/e9oNZ/wY4Kbv3TB9QCQ2SiHFPHNs9xy7emxlXc27asUml+OblqUr7gbeK6X89aDNB8KayE87ki5LUqELFCEYsCTbUvZPOiz53cW3LnLzwocYkehrAM0Jbu9Nt5VWBiYyVRi8XDQKNRNjUvnH0BUPvak2tkfWgpSUDSSdskhcAfjjuaA4gktX2c+6gjyy1NbWWsDNr7Pbl4DngDJgBbCtqe6id2zJw2OUTcC0sVLBHJyLkZdNr3wm92WeyX15/04vBTfywZ7zOWfw5C+PvmHhL1qWrhAS+Vmvrv9UKlJU2PnOTHvMea4gu7wL+Q3wXiHEtSlpnxe1lcdDqvCNMrKBFlJKwpqoveJXZ37/yDbz6GPERDmUCb/Ym2qrARirBri/cA7v0dqZ6fiwpc3LPQ8jkVT2R/Gl04oiob5SZUuV4Ny1DuV9/HCk2uLyxmmqu8gG7jrS7TjG2AR8wFDilhhYoJG3GimxCjMlmV5Ppz/PyuHKnnO5cKAGQ+q/rqyr+cXQ+xbvUrpublcHUKSgyim6ZkzdwnVH8DxcXP5bniS7rOpJpbr6Nynl3xO2c65HEWNUIRBC3AnumH84RmxOWdEKVkQTLdjSJkcL0KVMoNI+F4D6gZUMmj0IKSnvi5KfyOaB//kcUG24eLWzmez6li4uxwKbAXqUSGRhqho7WYkQaJGBOdpXm77MH3d8n/f0L0KRysNkczFpWbpiXhrzxpf0bA0LBfE/5/zPB28/cqfg4vLfM1SD/SNAD1AthPiUX1XGqEJkgFrg6qFIbZdXMWKWcio47QUx+AJtiR2MCkziVwRAQCTTS/3gBgBKB2IYtoMA1ldp7C6FMzY4lA5wgxtx7XIMsQEgRirnQhSe7DsVX8UyzJJHjJ/yCH8x86iJzP3Zp7qu+GplXY1sWbriUuCPa7VdRlJkQLLdEo5rRbi8q6msq9nesnTFLLKBo35gELi3sq5m15Ft2dHNiInyp/5y9cCvL/8FOyKvMCowaf/21b1PYcsYAqjoi5KTyiCBP50rEVJwyWqnl2y1HReXY4W9QCeCkjxl4NEzIvPPez60GT1nCwDnD5w6cHXPhf8aEuRLgPt2KZ1s1Vqy7xZ8rra21k0/c3nXU1lX0wb875Fux7uJEXNfA8S90ulJNPFK7xPErUFWdS+nRfYggHA8hS+TLcX58nid9gLBnB2SUT3cNqWh3h2AXI4ZhlLSVgI8p2995tv4V9a2fpLv7F3MDU1f4eqeC8PAAy1LV3wduL1F6eUZfcu+6NMba2trnzhCTXdxcTnCjJilDJD2BFJjuqL+7cpatkfW0lQxdmtVa2QyoFR1DxDKZJPE/3aWAwguX2lLwM2/dDkWeRF4jyWcU3XEhafh+RjxaWubld5NG9SmdS1K75guJfK/OiopYcJQoRfgG0e01S4uLkeUEbWU8+O+lqqePnQzm/9d1bprKqAEkxmCVo4E2F6m05kvGN8Kk1p5ZEpD/Z6RbIOLy1HCw0OP5/ze+6RTWVfz8997n3zhUWP9H17Wd45pVwewhbNPkAH+CLy/trbWzdd0cTmOGVFLeXSvtqIzT06c0drDK2NKQAiElExv7caXylYCvvfkrJX8vhdsgN+N5Oe7uBxFbAG2AZPIFnD5X+BDwOVkV8D5DtnFEXxApLa29rit9evi4nKAEavoBfDKrFMue3pa7N4LXzHpCfrYU5CzrqpncE7A8lre9IDWH1DkZz4vRFmfws9/Z3YKGDWlod58/SO7uLz7qK2t/QzZIgoAzwInkI1C/WZtbW3dEWuYi4vLUcuIWsq+dH99a14QiUlhLElhLDkHwBamBvDPhUJIRfC+5y0E/MUVZJdjnNuAccBXgYVD23YBtxyxFrm4uBzVjKilXD95iufZybmpqt5BxnRntzkoKDg0FXnMb3zC0vMjKrf8JoMimTyloX7biH24i8tRSm1t7SzgREAH/l5bWzt4hJvk4uJylDKiogzwxMkndd9+RrLwy/dm0IYOPRgocZZ+pE/pDUs+/gicv85aMaWh/vQR/WAXFxcXF5d3OSMafQ1QEIm9KOwcfnKVwkuTfGwf+x5+fYFnd29YEo6q8pz1FsDvR/pzXVxcXFxc3u2M6JwygNe2181v1C797SUqm6pNRvft6GrLaR8HcNEqIVRJF24FLxcXFxcXl0MYcUsZ2DS6N8m03TkA7M1vKLY0SUWXR160JgVws1vBy8XFxcXF5VBG3FIGniqOJMxZ20fpIGkvSJulfR79fc9bQpMM8vrr+7q4uLi4uByXjHigF0D95CkPPTdx1AUxnwGAbtnyzPo9QnPk16Y01P90xD/QxcXFxcXlGODtcF8D3DK9pRvFkSiOZGprj9AcuRHXSnZxcXFxcXlN3hZLGaB+8pS/JgztGiElPtPeAVw4paF++9vyYS4uLi4uLscAb5soA9RPnhIAKoHdUxrqM2/bB7m4uLi4uBwDvK2i7OLi4uLi4vLGebvmlF1cXFxcXFzeJK4ou7i4uLi4HCW4ouzi4uLi4nKU4Iqyi4uLi4vLUYIryi4uLi4uLkcJrii7uLi4uLgcJbii7OLi4uLicpTgirKLi4uLi8tRgivKLi4uLi4uRwmuKLu4uLi4uBwluKLs4uLi4uJylOCKsouLi4uLy1GCK8ouLi4uLi5HCa4ou7i4uLi4HCW4ouzi4uLi4nKU4Iqyi4uLi4vLUYIryi4uLi4uLkcJrii7uLi4uLgcJbii7OLi4uLicpTgirKLi4uLi8tRgivKLi4uLi4uRwmuKLu4uLi4uBwluKLs4uLi4uJylOCKsouLi4uLy1GCK8ouLi4uLi5HCa4ou7i4uLi4HCW4ouzi4uLi4nKU4Iqyi4uLi4vLUYIryi4uLi4uLkcJrii7uLi4uLgcJbii7OLi4uLicpTgirKLi4uLi8tRgivKLi4uLi4uRwmuKLu4uLi4uBwluKLs4uLi4uJylOCKsouLi4uLy1GCK8ouLi4uLi5HCa4ou7i4uLi4HCW4ouzi4uLi4nKU4Iqyi4uLi4vLUYIryi4uLi4uLkcJrii7uLi4uLgcJWgjcZBvf+0Poxpl4Z6kYggpoCS3iZrxD7Bq2zl8Mv1v2s3T7T9rp9ia3G18ITNBfla1VkYEy+pCO68sN3vGFDv3hn9sfWDHc86shU11F0Valq4oBf4M3FpZV3PvSLTxWOcDX3p01F7d3NBHOvxj/XdCRMN0R86i03xFrgtUMk7fnXzBc5J/bvtOUmE7fbJnjP6SKDVfHrusSwa3552y97Ld0ztOvw74JvCbxbcuuu9In9O7ibm/XyqE022esyapGuocBrSX6cpMRvMvkJN7HxWbQ4Vyc0mVZSvt6c+2zHvpz8W7qksTxSUXxXKbgM/j9vtb4rLaL6zbWbZ3diZ5AalxhRjtcQxDIRj/q7ykXotXdOdxw+kfIH9Hb/oSZ3M0lRf8Zm1t7R0A9ZOnqMDfgXVTGur/78ieybuP337pe77twcKEXw4yRdnGajmbxsxkrtX+iVQc1iqT6c6M7csgjf/35E92//nc66sDhtV78/Ufrtp3jJalK74HjAU+VllXI4/YyRwFjIgoJyzPiy97NAEOAF868UYUIakM7WT+qg7QNqrXpU5RoZptmi4iWKcAp5wWnUOZ50OoyiC/1386e2L6b18HvgPcCJw39CdGoo3HOr2Ks6xNFXnjRC/vNVZyi3kP+CCsB0TU08VAIuRvyymjraqM29Y+7NkxbgqzwfN424dGhaZ8h+er754xveP0B4EAbr+/afRU+ffn7HpJLRoMAs+SC4zhFfRMsTDTuzkhvVu8FJqvQ6G+3Tdwdl+mkj4NzkFONxBuv79FdlU/PVsAyVF+TN84MhMgv/GrJIOd4s4TCGbylpJ+MUqrpyi4Up1YMIe2fwJ3DL39POADQ3+uKL9JkiLQFND6AGhgPDnE6LUjvN9Yzv/yGXxIyj278/+ensd7C06b8ZQdgiTBmw8+zPeHHn8NrH5HT+AoY0Tc1xnUwEEHFdkbHcVjHbJvjINvglQxCIAhbID8oc0VI9Gu4wlTZPvOR/qg7UIJYTgmacWzf1tGDw7b46D7soO+R5c3jhR2gT+tHrpdpg/ZlhEHfgN2VoLdfh8hHK1g/3PF6N//fDCUs/95+lBbJOfVG1zeOJZQva/e5hPZ6z6JDwBNZA22mCe0f5+f33iDH6Bl6YrhX4iH45yRmlN+w3f3Fv/RM+EMPbpz3W8esf+fg5AoOCDEq3d1GUGkQBWHvbQP3egM6377bWvR8crwa/vwY81hrv7940395Cnuj2MEMDjUIAPI6Ac0V0EWDT31DdvF4ThnRMRPHjzi/0cOtRsOwhXl/x4BoB/mx6BKGzlsKJJv/OtyecNI4/CbD/0+ksPU2zy8krv8l8iDho7X6ttDtg9/kz6iDTpOMTAPuz2pHdBfiVI69NQ/bJfjfnAaGfF7E92YHvaDOIzVvG+DK8r/JR5x6I9Bkwf3tCPc7h1pHMXOO9x2OWxwEjJ7z5kUB4wBe99uLiODeGuWMnCIK9blPyMP06Xe1xDluHFAfx1H2TfXMFyUXff1O/2BqWHPM4e+vG+0OnRyzuX1EHDonDJINMc6yFK2hTr85YN3dvmvkMLJEfIww708MDhpMivBqWG/Ouu4twtGmmEdOtwLIeXh9tjH8PHGFeU3iRSH3uV7xWFGdyA2XJRR9sUQDRfl477/3/E55dSwcT99qAa47uv/kn13q95X3+pIiSEzB7msbeXAGOR29MgghRU87I9gmCirQ67s9LAdh3wY7s3QiPEalrIzXJQP6W7vazx3eQM4KIcYUR5x+DnlqH7Afe1I4YryYXh7x+TDDDXRYRsPcy/luq/fIr5X9apE4rHTWOJAgONwS1l71b4u/x2OsAOH6z4pD3wf6pClPNyXYbuW8ghzYOgQwy1l+9Avp7a2dl/vu6LwFrAV5ZDx+rXmlKPaAe+0gxIeeur2/zBGRPwcXmOSUh76n4NF+ZAfyj6NcIeq/5J9qQgHkHidg0XZGXZjq7k6PDII23t4Szm5/6m2X5QP/FwOb0+4/Pe8xtAxTJSdA/vsC85zReEtcLjx3/MaV3ZMOxAP6UixLxXN7f9hjIgo24jXLUKiDYW0RP6zpbzvC9nfrpalK9z55TfBIe5rJF4ndZAoW8NyltXhUdnDJt5u+cxTbr+/CaSwDjuYSHkgiuKA+3qYKL9KQ275zFOul+gt8FqZBWKYKNsHhpd935krCm8B+zBzyq+VEhVVh3nspLIvadnt/2GMyABgiUPdF69mn1i8WVHG/ZLeEM5QKoeXDLY8WE89ThpLGfZjUA/crWqH8Wbse9vb0MxjFqnYxuEDvQ6I8j5LOTPs+xmaUx7+Rvd6f5P8z5WLh40XB54edDUfJMr7u3vfBOdwURieM+vyBnAOcyOki8Nn4EeGeelsqRzOUj7u+39ERNkc5r4Qw3O/h/0qPENzDJH/7L7eNyC50ZBvEkdk82R9Io0lh+upxGunMcVriPLwPQ/OmXX7/U0gsfTDibJ0hlvK2YHKHHZ5H2ZO2e33N4mjWuWHf2XYMDIs0MuWrqU8kjjiUB3RX6MsTnSYUe1IZV9pQbf/hzEylvIwUdbVYXmZw/bZJ8qxYdsyhxZv8e7f/dBtLv8BZ2h+zEfmVaIsspbyQXPKr2UpH4Tb728CqVia5hzu53TAH7TPUraG3QoNOflcS/ktkPA7Uw78b3iRnAN1QIQ5PDfcFeWR5HDBihrO8Ln7/USGV7OTyr5+d/t/GCNT+1ooByInlGFO6eGW8mGKWtgHZS0D7o/kv8YWcsh9fbClLISKgjzIfe2oBwYr9eAfjisO/yUSS9NeJ5RaPUx1L+vQil5uv79JEh5lwv7/DM/BF8OKrFkHxqVh7mt3vBkB5GGq0qnCwXyd9Y4cKfb1+3CX9XHf/yPjvh42p+AZZimjHPiyPIcJkXdeVehCwzzcHM9x/yW9EewhT7RXmFgHVXzM/jAOtpQPiLImQQ6588TBUTJuv78JpDAV9XVF+VCX3mGcfG6/v0nSujpq33M5TIiHW8qKdWCssVxLeURxDnPZqxwqyq/OD7ek4vb/YRh5S1k9cEcqlQNfg+cwYV32q9J3/GTcOYb/EmdoAs1HGnO4+3rIchg+pyyHizKAzL4mDraaj/uAizeFsJTXs5Q153CW8iGb3Ov9TZLRRCWARGV4IshwUSZzYKxx3dcji3OYNSQUwHxVGXHlVfvZcn8aiNv/wxihpRsPjCwHua+BfdNsh7OU5assZQ8Zf8vSFToHF4U/7r+kN4I9FGxx6JzyPkt5WJcOG7hUKZCOhpCCV8VruP3+pjAV5XAmwzC0w1jKh0kccfv9TWKqohBeJcJwkPvatZTfPg6XdKAgD7GU1VdZyjauKB+O180vfiNkhnk9DfVg8XUUgerIbC3UoYqCChIHBV41p+wVad/vq43iT+46IOw7sAtOW/rggqG21jfVXdSPyyHYQzdY2ejrABIHW0ug2F5AYA6bU2bYc00CUkN1Dlkc57j/cbw5MkJ9nUXnDue+tpBYSBzAQNCmOkVVSx+cS1avdzXVXRQ75E0uB2GqIgyAODiL76BALzuDFD5kSCel+ulRclGkc7gFEVwP0ZtFHHrhCyEPsZQ1nIP8pZZUDle85bjv/5ER5WHPh7uvISvKIPFg8h7leb6mL6OQCKucySxQdh60r2owen2e2gLZ1KlbSPEQ5r0cCECKVy198ENNdRfdNxLtPpawh/rIIMNK5SR6i1YhVRMchaBRzM2+XxMUSVamT0coF+5/3zSniXjzLKZkxh50vIHQSw/9/Jv/ShpmITI12cQqeVYg/gw8uvjWRYevoXccozrm/ovU0XSSo8aDBKO3HS06gODwgV4tnl5u8QTICB1FShzBvfsud4F05n3toa3VprpyXlq9P0cqr5Ct0jm4+NZFbjGwISwlWxnK0isO2j5clB2fH3NhKXhUMsC/qAD4+32Prbpl8pe/mzt5xy78kQyBSPqsR798zx226jV0ia9PlU2b/u/CwXfwdN51HK48r47DerX4oKAJf8XfycRmYg3OAVTMeLj0D5998uYFOerl9wqLFZi041wUXfrgw8B1TXUX7XjHTuIoYsRFeVSg9aDX9rmvP60tZ77SuH/76eqmQ45TkM6Im9YmacLm2ySIiRhj1Kjwq4PYSppOJxwYSFfeO++7v+v15b24OpL/ytNSOK8Aa+/8iRUFnCkN9bJ+8hSdrKWXnNJQf1wMXpZA5BEh5pSyzciDfdMFikMsbxQBdlCYCuOLnHnQRMIHjRc4c2ACu9JzDzqe3tFvdMiE0a/HSKobyIQSV/gLo1eMVYRz2w+fHJgUm9XvkcUD+aK/3K/Ew0IqDhh9msjsFmRWOzJ3uU3hS5V1Na+zhPaxgWqbArLOoGTlOBxvAIBU5XiUVAJv6y4MaXNu31aqYq+gOmlsJUBay8VCklQM9vqqaPRXEtGzTgopUfqFPT2hJqc3GalP5ZNC94Qot7zONxc/3lJkK6s8iOeB54GNx6tQm6r0S1Ri+R87+AWhI4FUzscww1UA6BkLzUrhaBppw0ta9+RumDiVDROn7nvX2SIT2a32RxCdBkqvzZxvPMgoW6ZKpLLtHMW74hRVHyTr5msDtgGbK+tqjmPhPtRSHlA8NMuag3JbDW87vpxGfAWPcFa/jyvjGS1o+D9/p5zEc850tssKhtwd54Ozqeqb9/wMaVzfVHfRcXVdCynfWvHjE5a/YkRXtKdTAlBS3DLnh3gLovtfX/DyAKHkgT79tXUpD9oncaqyibCIs8w+g7v0H4Pi8E1zMafISp5QOxit9uE7zJR3r6ebAU8HrZ44GSGYnhpPZaqcwnQZoVQ+mLazIx1VWqRDypHENOxUMO5gDOKoCctS7USfbnVHDU+TFJ41Avt+xRpc0/jVX7xrq0Bf/aVHxSoj7fyk+zZ2VJxERrPJS3lRB+YTD+0iGWgDKcntn4mRySNfa8AvIrSYC1DJYGMgSDE59180yMkMiiC23YpI96IloijpJMK2wTZJexO0FKWor8owlQQnZWIUOA6a8CKFD1vkoMtC/HYZ3kw5obQ34bdlgw/xuEA+asrRz5f+5EP/laX9la/9sXRvoO28jJopEzgZHbk3gLWhIFW882f/s+R1nMdvH6VPrzfGNnwrfcUzCmZOHqmKcQBog71YoTAoKsIy8e3Zhpo5JA3wILKzCSUkjHK6jXJ2+UfRrUGfIobyQSXgZCfyBISVOGVqggpHNfPT/q158fDjOWNWeXb4o+dHM0a517Q8mnRAsTOWTr/lcXYEvPG1+b7+l6YVNDwfMuIdZy3a+a699gEu+P4Xe7ZPswtiBZ9E2BEUJ4Gtl1LU9luK0mextXocSMnCzUlOrU+hDp1t0hAMBBSaigV7iiV9IY2+gA/5qgKFOYkYhQOD+AdSOJ0ZctM51AgP89GoQkFBYGK2KagrVZSXgTXAs9F7/58g65q1gcSUhvojdo2+nXz9+p/IedMexeuJs2fvTPp69wfDY2t9eKwQFjoZ/ws8V9BOBj+KVPA5Fqen+/lwZJAJpkmfM4pdYjpflzPYlZkNwMy+rVzW86iZ7zfjWlDtMkLWdiMvsUpOGnxG5jn1QO+7/fp9NW9ZlL+z9Pe/vZvcT+nFj6Dkr+KGygTKgWBsZExn7q4e8gdMHrPn8f/MrzAalb04aNicp3UwRt1FQhw6hWkKkwHPABoWHtuDz8x7dYQwfukh1/ERkj6C0osfD4bU6CLDYBq8VoqgbqPrBoqiowAKCqoUCMcBx0E6DraUpAUkVSmjmsOgZpr9mj3Yrctde33qqp25nuei4dA6J1jQ7qjhVMeZs4+aC+HXX/nd53PXPXxz+6xZxMISxTb4HH/h3z0/IyO92Hmb6ff0o9ge9kZm8IoywId7ohiBCZhGhLS3C9Pbhv161VKlQLNVNEeiCAdF2PhsQcCGgGMTkmlyiOFTYnhFDK8SJaPb9Ht0ej06A4aHmO7BVPxge6VuGbaRUeN+y2kJ2s4redJ5qdxU1ubaxh5H5nUV/Pibzvu/encgL++JNZMUOSk3UyB0x4eQOlIIHBQs4SBVm5SaIaEl5YCacPrVuNUv0rG4o2558Mt/Wfh29/+Cv9/1zdG7//bj7tHXEMnJw59JcdL65ynftRlH1UiOnorjNcC2MHo7SKp+BgN+cKIYGZuAE0I1dKQBUtfYX4tHOgjbQdgKimMg0TB1G0fJ/tnCxhIWlrDJCIsUWa9VERlyrDAKh8QJDB02g5BpsjX1TBzh4ChIR1VsoZAUKq1Ctdeahvqi5ZMvl4S6m+aVbOg7a9HOw5dpOsIs/MWXnf5pp4gubQKB/n+S8c3C9E49aJ+ajW2Ud68HAX7HgzIU26IJiVST+9deNhWVjtwCWvKKaA0X0RPMhWExM14zzfzdDUxq7UKaXpK2H0v4EYoHXfUQUA2KpcbEjE1HTpzOcIIuv0m/4RATFo7lSCVjOVoibfli8XgoGu3JHxzcmT8Y21ra1bNhbHv7No+ZaQF6pjTUH35R4qOMm375STl9+tMAmKbBmpcvw7K8SCSf5y/8k8voI49K2minGHuYg9YUJjtydqAau5hm9TDFTDM2YxGzStlgTWBA0UkKgU96CNl+AlYArxWCjB9SCjIDlmNJU7UdI6dYpI0c4VG8QgqNjKFgCqQjbCSWdEibkmTCIdEuRKqhPqymk179BFtVS6WieAChSVvodsbRnbSpOZmUKs0+BXuPrqbX+pX0yhzFaiiyBtqubb8/Su3g26IBb1qUr7/pe9bmsaMOKq6ca0WY0L2GlwNbWFwWx84oqMaBm0LddDj55X5Oif2KbvKoQWMNCc41GslTsqvoWMIiqkcxFZOMkkbVm5kaDzNp8HRIjyMhNSJESPm6EZ40g0qcxLCFtCNeP3vzS+gL5CCkRLctVMfB0hT6/LlEvT7SmoGlqjhDg55AokgHVdqo0kYbelQZeo6DJm0UHFQcVOmQlQIHRUoUHATyoL/scbO1bCZ0dsobP/GNEV1g4H9+tCT1yqzJB0W0WEKl3jMe1ZRcsm4lozpTXJf/c27ruAMLD1LY9BesxdZSDDpe1lqVnGQnCBjd2XnnIaRpk2N6cexKhKNi6TFMYxBbTR8SzJFRNfr9QQzbwpdJo9k2AomDikTHFga2ULEUBak7SMNG6g5oEkcDRxNIJRtzYKsCKbJ/jjL0KJShqtCC3IRJ3oDAG1fR09lyJxJwFInUHGxNYmngqBJLk9ga2KogmLL5wWe/NOIrjr3v/t/J4RGnDgqbfJOJqYH92zzpJFff+1sK+7vBO4VEiYPtDx14j7BJaDEUR8dvD49z+e+whIKpaaQ1HYlAlftuOG1soeCoGqauY6satqJiKSq2IkCVSC37hypBkUhlKJ1RDK3SLbJW+b409uyD2P/97EMO+68kW6bgrsv+34j3/6eW/cLp8wf2Hzfq19iozEFIh0k7fkxr+eeIBrJL9eqWybndz3Ch9x5ycrqwLA/pdIDBwWL6+8qJRouwbZ0e1U9TURnJQBhvRuJgoFpxQskuLM0gGgqxt7CUPn+2XLPuZKhKNVNmdhC2+/E7SQzSqNiojkMwCQFTw7AUVDQcFKRQsBSBqahkFIGpKpiKwBQKppJ9zRYK9r5HBPbQb8ERAinYP+LwqudSHrwYx75Rfc76xsy3vnfjiNax/9offuLsKi7c/2F6YIBy2riQ+xHA3r3T2dM0h6i0uFHczI18kigHrn0psxHwqnAO8oVKJKZiklbT7AnuocPXQUpLYQ/V0VakgiIVVKnu//PYBoWZEKXWBFLB02gsyGdvyEvUGJGZ2ddEkTYemcGQJro00aSFLm1UaQ1pRVY3Tt65zfnJ57/zphb3edMt788LKS8ETzhk+7zcEF/iZQAa4kUY8QAT8nYBYOoKz1WOobshjxwJc5Q0pcZWAsLEFhari16m09fGe2NRxnZNQfadzWD6LCxzB/X2blTqGe0rY0FoBjnKNDAhavaxov9RXqwqZfPEObSUjDqkTUcaf+FLIz4gOV6Perj+B0CD9ZNGs+CZv8GpEFK76LdHIaRKIDaBaE4DuUqKRUY2fkICODo7nRCNdhGdMsTZKYM5mexl0Rl4nJ3hXewMK6R8laCOxtFKSHkLiPjzDxqQ31bCwGtUN/5PjLN28oORbgvwYnAujjj0d6ZKi6pomp05AdIeH3de/DHOXPEwM7pK8O15gmRuCd1lE8kVKVSpEjRzAXCQ7MwvpK+oiBLZw+h0M/npFnJSgyTifhp8k9gdqqI3UICDwFYUTM3ANLxkDA8pzcBWj77FpQ4X2DYSbMkfLXZpYw/ZPpXNfDGU4E/sYSX5FDrd1IpvkFd6YLrXMFIYRopQqJfKynoyjmBHwiCT8vA+uwBvyCZiePBZJrkM4FdjCM0CkS2P+qQ8h0e4mC6llO3+cWxn3NtyjiPFLP+eEb8wmgvzxOHGoPyOJCeXPE5p6Xb27pnJrnQReKCQfqKESEuV581qCu181gkJSL6euJ2kr5CoN4wQCoZjYDgG0/unM71/+iGfkdJ0OnPy6Q3kMOAP0e0LsNvjI+7xHbJCmJAS1bFQkEghsISKHFZ7W5Mm49hOKe34iaNiY6GRxksKH3H8JAgQJ0gSPwn8ZIYi/B2hkhQ+kq8TLD47Z/ebHiTftCgXdQ/YF3uf3T8iDapBsSJ/HmvFAh6Ql3MZ/6Y7bhHecDYbRvXiMRUmz7sLURHnf5tvJz9p8LJeSUCopJQUz5Q/Q1JNcOW2CozeDzCglGMln8VOP41AYVLufKbkno+hZt3bppOmOb2JB4JtLL/8ffQHs3fEQjpMsuqZpNUjkKTwYqGjYlPJXkroIEQUQ6ZQLZCWjmPpWLaBZRvYjoHpGJjSwHR0bKlhCg0LNWvxZe9/sBUVKZUhS27IVhZK9oKQQ/by0MVR3B0ZcfeGmkiaF3c/e5AiKIomogGFp70ns7FgKq0TPNzadRa290XyhM3uMXGmT9xM/64w29ouJkexKJAh8tI5LNBuASXJv0UNT8pZrCmMMKC3sbv4RRLeMLH8j2N6Jx+2LXrGQlUdUqpxyGtCOhik8ZDBII1BBkNa6NLCcCw0aaM5NqqUqNJBdWwUJELKrBdCyuwttYAuX4geLY+oyCGNBxsVMTT9oWGi7/uT+/4sNGlRluge6e4H4KKeFVIOm0YRSBHKbaFGf5LK6HTU1R/lwwt0+nJyWH7+VXiefJyalwR9vXEK2p/grtMyhJQMBX3TyPgNFFVj0NpBp5nHttyppAtqsLV8UN68Be1zTBQsLKGQTT+00bGG9VMGY+hRw0LHRJM2uuOgOqBJieKA6khUKUGCcCSKHLKEh7ax7/mr2d8zAiEdYP6b7+DX4aTWRjnV37z//4pQhaMNcm7uvQRK+vgQtzBLzuREsRJDZBgwNXZ0VGE3XUFzyEN1pomKsifJL+rE8FpMDaaZGkwDEQCK4LAjo0g5LEo9wwXmZjqUqTRqY2lRi+hVQsQVDxmhYwEpoTCoeUkJHVuoWY8cDtrQdbnvGj1wrdpDr9mocsg7J7Oeuexvwdn/2xBk+13Z55Ubtm346D9kQ6PGEiN+ZzS+pdMJFj27/+OWFy0UAHtbZzEl/0nCRpqc0E5OTBdzt3IZ3ekinFgLncZCMmqI9UIyA5VmbH7q+yDTenYxv7ue+Z4gvlgnfT6FztJc+vNysHSNfl+Q7SWj2FNQSm8w9zXbNSbZwtzEGibJrVR69pDr70VRDr5GLanu/xV4SaFbDnYyiEwHkaYXbA/YOjgqUqpIKWBoKEIKTAQpRSctdNKKTkZoZJQhrRAKtsjqhY2CLRQKO968i/stzynX1tZ69toy9dCiy/HLGL/KfI4/7ypl6W9aWHPWp5lU/iyB8WsZDGvYKZ1tu06it2c0+aKTv1SuxhGCS165iOLMSYCDGbsPx+7Ao/g5q/xiQnp1tjPppDXwJP1TVvFY/iLuFB8GICz7OZtHOJ2nKaAXAG2PwNnup2uggmgsl7KebkLRGIopySgGCZ+fuNdLwqeT8ipYhoPQTDykyMkkCGXS6AqkDY9Ma5pp++kVPtmoe+QaJ6iuSxVoG3x5kR2Xv2fVf47aeYe49cMfe88Zoz54z5cXRljnGc8F/fcyfdkakmVVjK+vJ+JXMQNQsKCPwql9FDZMIL/56yB0muihXnTTpnWzruwF9gaasREonhPoKvpstvqXlGiYVCf2MtHcRpW2h6nsIs/bjFQtLFQyGCjpHLz9pXi7/Xg7HJRBGzMDCUsjpnjp13LoUYvoVMJ2v+5N2arT7CjJ50NKz2MnaBuf+7D+VNer52lu+cxTwrH3/jtjrrvUsVsUVU2iCBtQcBwV29GQ0kAoflQl6KhKOK2qOX0GgR0+PCuu+d0nv/t29//d3/j6BWO33vdQ3xctUKBw+3sJ7LmUn0/ycPdoA9W2uG7ZH+n2Pc/qyUWkjDi2YpEyQsTDV5LxzUYqh7/jFlJSlDSZGnFY0OcQsgS6A15bYogoFLwEuRvRArvwadH9g/X+9ycUtG4VrUdB7ZaovQ7qICiDIGIKphXENEKk/Tky4c03E0Zxl6oGNvpQX1CN4At6qOrlhb+5NPF29+Fb4ZYv/FEW5Th0lb5AUVET8USYWG8hU9ZtQe/qo13XCSUUwolcdk7/CL5568mpegHdM4g/aePNOOgZBylEdgrGVOgeyKW1K0ysz4MZ1xEZPzNyT2d8zlzE0E33YKaHzsQuovFWzHgHTrwbI53Aa1qEkmmC6Qyqk3Ur26qKqWukPRpJn05/yJA9YSPZHdJ7e8Pa7ohP1g8G5aaUZu4YNAZ3Ro1oBxDfdO2moyZ25bX41O+fkg+My+es+u1UF1zPmUVpjO0C9V8VxJd0YJshNP8AQkgsR2VzzxT27BzH2IYiSmKSnGQfph6iP38cCd8Bl9je8CDPzwiws7z4oM+riCapjLdQpu6gNLSDUk8bBfSQR98hS2Ck8NBJKd2yiAE7SML2Ymc0qWWwvaZ0DOkxfECABH6ZwCtTqIDtKFiWSiajmsm0NhiPK83JuNxmDWa2BKOxHd50qhnoAnqAwSXLlo9YEN9bFmWAn15zmfzNh75N3GNw6frn8Ax2c/LquxmXW8WJk14g5VF4bnYJqic7fxmP53DPrtEEehcwtmcaHicH6cSxYv/AtmOENI0LKq9CiEqQcfaYj7FrRj2p0RaPiIt5QWRjd85NP8yH2v+OFoceWchWfSrP5J3J9oJxOGrWmMxL9TF5+y4m7N3D+ObdjOvYQ0lfD4opcRSFWKEHsyqDHOVYyXz/nli+/yk7oPzLHxh85uKL1r8rAi0AfvOjP0m7sJQfTSpjjNzFdav/j+7NeTjmq9ZW9pWhehahFAzirVpBqHAXupHE0pMENIvNzGS5uJytYiYAE5wGPmX9mgq99XAfixrPw7OlksCqBIlInNbcsewMTWV3zmgnrZr9hXrLM+/xPHTTKdqWNdQOJt/KOd7ymaeEX6EgqIgSW5KKOLLLlMQW37roiA5cLUtXKL0br7MzYxJE3m+jmAHGPftzhGPw7ZleHivLBlyFknspibVikEtSz2FvTim2crBJFsykKUykGRtzmDegcH6XIHdYrHra307v6Ifpz92GEuhB0Q7EXilRMLYpeHYKtPbsnxLNlk+1dD/xYAWZYKVFsLhN8Rc95QmP/mtZKLyisq7mXZ1y8sMv3xK9xjMt+Ji+gU7RRbiog7HaZqpvGjwoMDQ9waH/Yw5Ork1lW4rqPQkM68ClszeRzwvd1bQnfAy3O6sC05mTfyaGlvVcJNrXEt/5OEp/E5rtkPTotOYbrJwiaSxzsBSdjN8TRfPEpKIMOIrsMBVzZ1yLr09pqU1A/aZrN709bpwjwNdvulf+dWYV0zt6qWi+nmvmtSMEFF+vY+dL+j9h4QQh3lGAvd3Cu3E8raErSXvzDzqOlGnsTAMJbS9PzJ/LlokzAFCk5OTuNHM6n6XC+ziF1R341QNDSVoa7E2XMkAeGXKwrDB6spScvlGmvzvcGohlntRjyu+6nNCaxbcuOihY8carLh5P1p1TQrYAUxToBJqB3UuWLX/HU91GZDY8VuCJTW7eE1w7fgJ780s5qTfOhrGC07dthUlwe/w8dr88nupRW6is2EogEOHK6nbaOotJOTlodOOk7iBtm3hswZn570OISkx7gHUT/kBmdCd/Mj7HVjFj/2d+pPFvzNZf4VujfkKrNvqg9oyVO5gvVzHL3ERJIgUhP56cFJ7xEbRpEQZyHMwCZCwU3GM63kd0g9v8/siG8xbVH/V3pa9F0+DOjiszVaViomSPGIuxwGHK9J00rZhIZHAOekbBF1pBeNw6/EUr8RWlDnLtNDKJO/gw20Q2alVImzN5gmvFH9B0G6TAEx2Fv38S3t6JJLpakFvWIKMJ+oLheCRYtSY8LfbvM3NWPnyFdv9OageH3Tl+Z0TOcUh8e4b+jhoq62qc7deGMyXPpIzo+Q5OKE6seA05HafwPxtT5JqSu0YbRH2jifoOvlbn9llc15imMiEJWBIjGz3FvrWAJTbJ3F1ESlcxmLcWS4ug+g/Eryr94Fur4NvkxZeejyyfRcznQYbTpEMqihFIqr7wy4FA/p15qvpQZV3N7neyb94J0k77Ro2Zp1xozmXDru8SXNCM5Yf0t3LwbDsHUzQTndBAelQEb8pm6isJ8uLZ9PmUo9GayOG5jir6Mv79cRKK4xBKOywovID84mwOvx1pJb3pTqzuelI+weMnwqPzVEIJD1UdASq7fK2n9qp/Av6xZNnyhiPVH+80OWbWYdiaG2L6qnLsee1oQPw0m9x7NIq+46GjwI90PIjQueyqXASAkK2Iwi0YJMhE+kn29tNaWMHy865i0J+PkA5npdZzeeo+cvK3oBQdGK8GZQ6N8TH093ucGS3zOy6ILihXUZBSpoC/CSF+C6ytrKv5j2P6kmXLdwBHVZGSERHliGL9s6S14VOMn0BvMA+0NO3lE4k808D3kpfSpVdT6gzS3D6Fvc0zOWnucoxgL6NO/xk7njyZSKdESZtols1ZuQvw5Y7BNPrZM/tbdIcruZGfEBFhNNvk5J5VnFn0MHvHjuNr6k+xhUquNcip6jOMYwcTre0E4ipCT3TrvsidhGmxgsYpVqlalpFKXEr/Vl1PPahp5jOXLFofH4nzPxrIb938i9Vzx9fNGgixPk/j5dgpnB98gAnn1QP1OI44ZH4l1unjxdRslpecRq/vJAAUx+acPXv5TEuAEqqxPF9DsbwYiTIyva3YzSuJeO6kPzxK5sjxt+Z2rfziguf+fNxX+MrkVGwQsvME/ypB/GxJ86h7Se08gUpD5ev1ac5rN9ma47AjbBEkRmkiQUW3TlnUIoiHkJOLhootbaJOP6mi9ZhV60mGt2PZFqpmg5qVapEC7zoF3yoF38AUPKNOQ06egap6UFEHAvBXctlCNl92Q2VdzVGZyjRSKGbf85YnfYqmeIhTyOTN7TSdYNJXGYHKu/fvlzNgMmNzHK9jkXY0Hm6dwM5YAfsLBgoIJtPMaO4mLyPxnXQdWtEkpGOTqb+P1I7H2FHqcMNigWH7nLFtQeXClX68proSuAG4f8my5cd0Xx+OcCbraOn3GZQkc3EyBngyRE5XkI96yUlmkM54msvLMZUBiN6FULuw02kYgAQgVIdtZ83i/ur3I4VCiWznc9zEeO/2/QV/eyhkvTObzq5cPAPt/Re3XvDnmVblx4UQ5WRzwW8VQvyosq6m48j0xMgwIqK8qbL113N2F3wKoD8YRgJl1hQ6Qtuo6B6DMnoQkPQPhpgweCK7uxZQUvN9cot6mXT+s/RsDdP+UhELSksI+s8gUryartF/ZGu4iv/lO6SFj3HdrXx7xX1UttTzh+lns2zRZQCcKF/gE+pteE2beN+ovd5wZLGRm37urEU7IyNxbu8WNNu6/bFwpu60zijr8/JYaV/Me59/kMRpWYNVUSSWpdPcN5aG6Fj0lgSiQ3L/+R+i31eIcBxqdrewZE+QCrMQgESiH9nfQtpuoE9toT1PpX/WeXKa+d7bp6q+L1T+sabvSJ7z0cRo9fw/SOWVE4KPqkTPBC2vC3PGP1i1/krmSC+zB2D2AKSaVDQlxJN6E9vVXrYbUJEJocb7ifc2YmtRxl/Sga8gguMoSFtB9WTHeaNe4F+hYraMRhScQEHViSj+fCQSgbCBm4EfVNbVHFf14fV45wtWMI2GB9PIpzrRgfpiGekcmwIzgifjYEuNsJmdGu9MBrineSpx28M+QTakzfSWTkr7koDAe+Jns4JsJule/2vuGbeNxy/UYtN2FxjnrPEbwZSmAC8A3wWeWbJs+bvWy/ZWCZrSKUk6SqdPocvrJbluDMr8JjSPycZzZmF2ziOdeg6cxgPFvywAidcnKBzVw/pT53Cf5yoATrZX8vmeFwl0jOdPY8fSFpzBHqoJdEaYEn0wMr599Hs/krzkQuArQ1/fy8AnKutqDi0T+S5kRES5oyCxK7Q7iiIlMUMnrQXwWbBi0YVIPVvdy0paTBg8EYBm8RTBf/gpOCOONTtF4bQBCqYMkMn0sF1bjNTStDCKG/gWaeFjbksXP/r5j/AmBrjtyg+w7MysIF8p/8758WcY7JzZEBPWeVd/4o69I3E+71JaKtri7C1+EtW5gh3hQl7MP4MTf7KOHBlFqtBmVrD69DNJGwYpv8NLl59I2uMnLxaj7pUo85J5ACRSzfRV/5Pust301ufSu6sQCs+Xp+lz7prn5C6pvKGm5Qif61FHyFu1oqvMwNeaIbMlD+/MXvLHPs3cxDOs02oItnyQPPzoiuRxfSMtah+KFJR1SxIDOxCqgRqqpvrsp4cEWaAoDigOaicE79HYlTyTrrIrmHKSnZ6kBfbnngrEOuDjlXU1649cDxw5/Kl0U1IReAHdyKEn4qcq3A4H3Zpkw0MaIqX8y1qEz8lGb/styYw9beTHUgjAKpT4ii5CL5uN7WS4KfwrHv/Ibsa15HS9Z0VBsW4rAGuB7wEPH89ivA+vbSXGxZxgp0+hJ6+QjrYy8lpNisc0UTylmV1N2dkmzW+QP6uHTf7pdIrJ1DbtRB2zjPvHn8Bf+QQAV7VtYsnmyXQ6uXx6vkZbaCKqbTG99XE5P77+k5/d+/nbgduBy4c+vg747rs9LmI4I5VhHe3N89tlkYTamhtANaaAuQapZ5dHtp00xYPZ4Kym4FOcsvYlJrX3Uu/Lo6eliFGnd+DJMbG82V9RMhrgZ8a3SXoDzNizlx/f+B0MmeHWL7+POydmBfk9rSuZ1OxpeiV52aXf+cF3j4k7pLfCkmXLZd3HPz7wp+njw9MH4mzID9JVdCYvX9dL0UM201bupirdxpfv+COPnHQ6P7v6k5i6zsy2Hm5s8JJnBjHtQfrLbqd/xhpa1xTRffcYbI+fsrLTYyeLaSeMr1t03MyT/Rds7y8rwtfaSv7qNN2VAQL5cXR/jMtb7wPPfexNz+MOcQIp1QOOzcRNO3CSbcRCARxboezUVnwFEaTMejZEEkIPq4gXDDZO/ijJirnMC7BllOadNvSZEvgR8MPKuprjeQqha0BK8gR41QArto+lZEwUQ7Hpz/gYNLP+z07Gckfu2ZzX8xBIKIgkWbC7DQFkRjvYBZJAyzQ8Ey8G4OcV/2RFzu7U6esKu8e2B0YBvcDHgQdcMT6AKkT7uJgz4cUi6M0vIb2jntjWj1M85nvkjG7CV1iFryBD34Iifub/BplMIU++VEdi3EruqjqNX/JVbKFxVmMTX91dRUIk+PqsXNoKK/Cmk5zZ/e9tJxbdO+3i+j/qwL3AeWQXZrm2sq5m2RE9+beBkXFfX7tJfuLGa6Lju7rDrbkBmgvzmbd3Lquq7iapR5iz5zJUDDp8G6nauYrq7gH2fs6geU0Yu1VF2zCDsc6Hsbz9dG2+n+9f8kk6c4oo6evlhzf/CEan+fsnz+LO8PsAOLVxS6a0vfPT3eh/qf3Bd90fxxBqZnDN5MTMs2M8C1zEE8HJ/Hb7DJpPeIitxQFyVzmsHncCv33/JwE4tX2Q/9viweNIusc8Rv+4f4PpofGB0SQ6vdgeP7NKL2mbJqpOqKyraTuyZ3d0U1lXYz6/eFKqnFZvaEeabT3VBPIbaarwU9aZpiVRxN+UediGB2GZRBMv0xSGoimS8soOfMU2gfzsKo1CgL5RJf92hUTaYN3Mz2MGxjPfrzZV6Mo+Qe4BLqusq3nxSJ3zUURPRKSAHLyqn17VQ9lLGdZWT6MnHGBQq2I3PrzpOBf0r5Q+OyZ8aZO5e9qxyh0G32/j2argW12N94zPIYTK47krWRNc1XHhivJoOK5PAFqAs5csW77tCJ/rUYcj1caquD0BYCBcjGM+SLwzh77GHPInRhh7Xid3Zb7Ng4EpjE4M8NCWxbTN7OSR8NncwpewhcaCbQ1cv7cMAdw4wUN9WQm5sQiXxp//0Q3X3PDdlqWXasAdZAU5AVxSWVfz1BE87beNEatFFjHibZM69oafnVDFmlKD+U4QX7IKYfZRkBiNKWKE25+nIpniuR9PpLR1D3ZaRfHCxJ6r0AL5JPfs5JZTLqKxpBzdMvnuHTdif6CfR2efwJ/UrJCcsHsrM9q3X1lbW+su3/gqhJVZNmXXnrOXzUjjsdK0+j1sluezsPEMJJK7L5T8dkY2DeGKPUm+0aBgqhlaZ/6WWOka9I6prH/UwUwJRKCYcwvfs6NQyTulsq7mmEnfeDtRwjN3OZ6np6oR8PU6pOJ+CCR4pHQsa5vOQ+oe1HQKO/YC88YnKTx/AEU7+J4yk/Hgvd2gaHWauKGxds7ncHzjmeRROisMpWpot/VATWVdjbvWMrBk2XLrn9/6EzjFeNUA/V4fhtnLGVtWHbRfzKPz3KRRAiGY0dKNNcNm4GMWvqd0oltnknfmh1HxssW3k/vy/rbtwqdGxTyWMg9oBRYuWbZ81xE5waMcn6NsLkqaF4GPaDAXkNjpdbStKiY4OoURTDKfv5GOX8A3+39B/TzJNjFjvyAvevkFvtEawu9TeKbA4f7qMGO6OrlqoPd9X/n0t+5uWbpCALcAl5G1kC+urKt5+oie9NvIiJVg6/R07coZbCGccejzaayY4mNu60JO2TPk+o+9SCqo8aclVzE1sJHeLdn5ywXmdLRACVYmwh1VSVZOmAXAtc8uI/8j21hXfTq/4qtIoTC5vYm5ext/4Qry4RFSLs9tfBo7eBEimTWg/m+yTntA59EKHzfOyJbGfP/eDN9osEh5orSc8k2S+dvI3/ARNj7gxUwpVOUv4H1F1zYXKnlnuIL8xhlH6bpUZXZN38q9bWzbfhpSCnzjBpk+71nK87YyPfgop1zcTvGMfhRNIuNBkr1jAEgmA8RuKqV0dRpbCFbPfL90fBPJU9k+yauUDH3MBuAEV5APZlAZtAECZMstPjPrRHbnV2ENlUO1VC9bK8pACEoG4/hmxOj/lIXZMIaXY18jcNIV+MhjUI1yV9GtbSc/O2rnkCD3Aue4gvwfkPa23FR2SncglIvly05bVqkDDGyZhG1rTGQbH/P/go4KGBC5/Nz5BrbQOP2VVVy3YhUFvmpSwqFueojKrg6+syV19Vc+ffW+0PkvA/+P7HTNB45lQYYRtJT7jcHdA0aSr9en+dYsHy9N9nDCjhSBtMS2e7AzW9hwyUL+n/ob4h0+Et0+dDRGlWbnmld5mlg9YTZJXWNCy05OqHiGreu+In857yRhagqVfZ2cvn3DVgHfHKk2H2ssWba848YPXG5P7xhQ15VORzN76fUWcHlNcP8+c3p6qIoaNE6+D630CfRkMblrrmXlnsfJOGnrjNKrtBLfmBhwYWVdzeErhrgclhI7v35LwUmM3vkvcjeYyII42zfNZ/y0tYTDnYTDnfv3dQa8tKz/f5jxUsZekC061vV4GQuasgFIq6bNRAZPEwq0nRbUxg9VkYoAlx5LQS0jRUTpzwC+oJadP1bNdvaeeQ5pqySxx476zWgjykAjQkoqvYMMfsgm2V/Nrp2fx1fS1VGeLC0F+Ffh39LjXsx9QHOUT5NdM/mSJcuW1x+5Mzv6aVP6Ns20y1EdialpxMvHoQqHSdojTI4/TXS9yvaxAQZzdGLJXH4qa4nk5DKmvYUld/wF7znfAAf+Ue0lTpqvbWj/wyU//tg/AVqWrjiXbLoZwJLKupp7j9iJvkOM3FIagqaY1+acDou/V9lszVV58YQos9YNkNP6MPY0nU8W3IaKQ/ParDUxS5mIYgSJOFFeyk+xsbQMgItfeoD2xKedv1wwW4n4VcKJKOdsfdlSpPxwbW3tUVHa8mhFSmfrmMZVM16pvBR/xqEg0s3unKKhFx0aC3x8JOd6hL4brX88iacvYE3P3djSTp1RepW32DdaAldX1tVsPqIn8u5k957SMVR6FdQBh9GRHezdmc+GxnFUTinBV9CEIwXW3rl07bkIEJSd+itTKLYeaQsx/vluVClpKg6SymYYOguDqqKI/ZX2r6isqzmeMwxekxg9g4DP4w3gzZikDJ1I84v0+4J+LZNBiQ+CEFTEozifHcS2fLSu/BStBR2RCx1/noLCmuDL5G/f8+OMmf/9ocNes2TZ8pVH8rzeDXSoAy1zHChJSdr8go5APuVmlEfEGfj1OOt9U1A2CdYxgw3l49k1YQxGJsP1v7uJ3tMvY6aTT4tP8Jdqgy898mjvWfqpnwJoWbqiGvgnWY/uH4FfHLmzfOcYyRVENuqaTsqK8dnt2Wo5a8rLKBj3Lwqm7GX2SRtRhUOgWdLXkl3Ga3R+tlLOZqONhorxOIrCvC3rmNro8PCJM5TmIh2PaXL+5pfw2Nb/1dbWrhvB9h6TCOx7q3esoSySJGIo9HsK9r+mIPki/0exvhszns+mh8ewsfsJLOkkF5VdnSnOVpv6SWVdzQNH7ATe3exOeFLICdmby7KeNCnDQfRqbN0wSPNzX6Ntxffo2nMxpmLKnHl/fDq3YoMuHcjcH6AolsRSoHnsdSZCMEYXT+doSunQsX9WWVfzxJE7taObhNPfAqAouczo6EKzbNR0CmOgByURASEoj0QpvrYV26vQtuqTRKRFKNS4bly62pMSafIz//prpjf/K0OH/OGSZcv/dQRP6d1En9cSlCezScgZaZHUDQZkLt8Mf53vVn6Rl8U8Bo0Aq6qzcYqf+fc/UMJhZuqnAPD96V4ueO4RLhsoXFhZVyNblq7wA/8G8snmIS9+vepcxwojKcqbAopO3BrkpF6bGXtbsITO7snjGVXTgapJdvRWs3zVqUgEo60c9FAFNg6vhOJsKKsE4JqHH+aFEz9irZqYTcNc1LCGcDK+B/ifEWzrscztquOw6Ok7MWzJgCf7FQcsyQ+2DDBpyyS21Z/C1runk4i2AZpzeskV6wu9FTlk8y9rj2Db3+005aEjKrPL2oW3JdgycyZStcmNWmwJ/dx5etw/eGbsPxsqFv7oorKxK88EaF9dSHVjNp9/W/UJpumt1BV4eZZfPX3fcYFvHIHzedeQNiONAIrU8CyyWbitmamtPYzr7Keqe4DZ7R2UfbQFK0+h7YXPEe2YSv2onasu6D99IUC/54n+l7fllQG5wGp4W1b9PCapra3N6Gaa6nhWlG1F4+lJc7GFYEJXC5evfw4hbdaMm4upaUzb2cjZq56laFK2WMjdlTpdVjef2tDyeNUfPrll6LA3ArOBbrIeouPGQzpiorzp2k2dXkUzo04MASzaka0v8aJ9OmZG49GmM/j5umvwD2ZXcpqkjwegWellXWEJjqpy8sa1RCvPl/cvyNMQgiltexnT1wnwtdra2qN6pZqjiG1Asrx9Ox9/rJGLd/bzvQ39/OXpJkTnVlb2+mFDMTLZDahMzr/k6TJ/9clkoxqvOc7zXd8qHVOd0rRp1GAHJGoSZnU1s3li1vs8ttmrWGLdrvljnr6gILf9PiFgsCmI+nyIUMok4dHpKbtKBxJn52hJIYRONrjlQnce+T+jJpKb0tkyUaQW5JA5J0NhNEVpX5pAgYPylX6sAkHbyk8Ra59Ff26Tc0qGWSVWAYNqN9ubttwM4hyy88gfWbJsudvfbwLbSTExmhXl7vwSAolWegM57FvvaCB/Eg3FxSi2zZf++Qf2nnQOJZQwoMOvJnq49uF7ZF7pCVcBtCxdcTbwmaFDX11ZV9N8mI88ZhnRBbB11I5uPXstn9weQ7Ut9mhV/P6VL3LX7oXMkX/DYyn40ya5JfMAeCEYZcPYySiOw6k7VJbPmSr6Qio5qQwn7doEsBJw3UhvkKGiBmsAwh2bmbEuTk/fc7yoNdCs9VIU00kOZlMti3POY1buxLOG3vrVyroaN6DlLVBZVyPL7PzdA47AmhQAYE5zMxtHd7CnJIEiBaevKRk7Ne3dKQ30TFRjz/NVVHVnA9xbKy60bc1HoSZ+6lPEPiv5D+738vrkR/SGATUbkG41zSV6iU3H/5ns+bFA/2wndhB6nruSWNt80p4eWsqeazxnYKEXwLYf/1N7Uu5zW3/LzUV+8yRFWk6KZMvB7i4fjdpzM2m5mYA5lgXa6dwzZSIAH3zsfnypKDNzzwXg9+M8jN7byJkDgd+P+e1H+1uWrsgBfj902F8fj1M2IyrKBuqmVn/21ihXz+Xcl1YAsHdcFbllv2HeruwSdrMyRai+fNKYPF1oADBnRx+9OdXsc1uf1rgGj2VKYEltbe1xMZcwgtwKYGcaUC0P3vgYEiKNait2pm0nAJo+wT45b/oqsik2XyObB+jy1mmKajHEmEkAlDX2UB2dzo7qacky/2B89Fnt+CuTimMJdj47gdHdKfKiGdJ6wGmtWKgCj54SUC8eOlaCAxaDy38gkNKa2/UuAFJNF6J0Kvj1FIX+PuyEQvrfp9DbdTa2kqapZP3A/OSUySoKg1pD7+O7m6uBILACuOlInse7lUGRsMbFHBRHMhjKYdGOarbk7GG2p4qvzcshranM2baFa5f/C3v6h/ELL+1ewd2jdK545hEnb/J7Fw8dqg4YA+zmOJ2yGVFR1tBeaQ9mA7o1fwGXrsje5PTk5PGNexQEKr60SWhCdsxZ4etk/YQpCMdh/k6N+072mgjBlI5WRvd3geCntbW1bvTjm+cuIIpMYmfqCcRHk9c9MxlsSqqO3QXooJWcPf7/Tj+psq5mdmVdzU+PlyCKd4CdfqEQNC7HygMtIflYaxPFFaN8nvfFRd64KLajsG3TArRYOVO3Z4Op6ydfqziqp/sEv/ozIcTcoWP96Fhf4WkE6ejUsh4Hv/Ti/PEUnGW5mdj94d2FtbnJveYHAYjl7KAr72lj0WB2VbT23vZfA2cASeDakVys/ngiIlIRjwPT+7NBvq0FM/hI89f42nyDXq/G6PYWrv/dL2gtqqQ6nL1h/eNYg1kNG5maMP5WWVdjtixdMQn49NAhP3685uKPqCiXmYUrenLDAATUHBxrL4FEnITXx/rx2XV6CwrnkOerICUcbp2Y3Xdh//PsOntNui/g0YPpDCfuXI8m1Vbg+pFs3/HCkmXLTYYC46zEUzj2ACR2+pxMtkS4olXWffHPS585ci08pmmscvLwpEtRxs4HoGJHJ9cU3kbYN+hPOV42bTmbWNco5q/O3m82Vyykr2CaDXyg3FD+d+g4EbJWg8sboysiswvD+RVBR/5JVD6bNCY+kqhuqniPz1G9mFqMPUXPy8nJk/xeaWCJxM4Nfav3eSV+vmTZ8mNurel3CktRBgAWdWanL+9e9F5+cnqYTr+Xor5ubrzpxwjbIj7vOvzCoNMjeKBC573PPCxF6fRPDh3mm2Q16YHKuppnjsR5HA2MqCgviM3Y5NOzGRyG4uEf5+aRG82uH7197BSSgVzCxTMB+PWoJE2lZYSsiHNK/pM8rp1lANQ0rsVrmjg4V9XW1iZHsn3HGT8HNoFFJvJHrOSz2a3C95sv/+NmtwDL20ejBx0bh1BedvEUT4OAbo32tolsWH0Rsd4i5qx+lkDKIhqoYOfY91jAhy4L671kI04hayW73os3yJJly824FbUAAopgIG8Suyom0V4yg/aybNpNf3grm8JbxQX9pwHQFW+/H5hDdprgZ0eo6ccEjqAD4IIOSVVbNi7L1D1Ute7llzf+gGA8yuYZ1zHV8AHw12qDU9etoiAjn1n46+uslqUryoCrhw73oyNxDkcLIyrKQFeOrcoYWRfGnPZyxlnPA7Bl4hyUcBHjnFK2BxWWTcwWtPiYeptyJ1dbCCEmdbbKMX2dlDl5z37v+9e/MMJtO65Ysmx5huzyZvuqclnA15DJxa/9LpcRYBvAoEiiBEuI5o+zhSPw3pS/ffv2BQPptK9n/ManUqPbB7EVgy3TPoGjGl9dfOuiZWQLJADEgJ8esTN4l5JOR/oB/GrWA7127pd4ZdZnAUj4W1lb/oRZlZ5AhVmMRMZe6n5g6tBb/7Rk2fLeI9LoYwRHYRdAgQle0+b7v/s5N9/wPf7wP0sJJhOsm/NVQhXV5JC1ku8vV7n82Udo9pofHjrEZwEdeKGyrmb1ETuRo4ARFeXKuhpbyER/qy972AkDZZz2WAuedJLBnDzaxy8kYRh8e4aBrapMlxsGk/jZISZpmm05J+zaJMJOQE61Kz40ku06XlmybPlOYAKwCBi9ZNnyn7pLzr3t7AUylpJdvzc26QOqBCu/b2DCVcvu3LDokTt75zb0egG2Tr6GhL/kl8AvW5aumA3sm0u+wbWS3zxmtH+biYUhVLxCZgqiDr6MJKPHnI3lT7An0KmfN3CKBEhbiYdMJ30e2ZSzXxzRhh8D2IKdjjNU/7p4NNsnXo1UJ7G+aiyr5/+ITGAUs4bq0v12vMHpa54HxWn7/LJ/t7UsXeHhwFzycR9oN9KWMkI6m/bkZl0UQT2MlVY5ZU12ha27plRz4cIAu3J0NGlGPsBfc//ORwGY39SgFKRtzjCnPlTz4/e5NZdHiCXLlieXLFv+9JJly9uPdFuOB4YCs7bn29mqdd68UWwdN+85QApYWDzIJFsobJ18Dd3Fc58CvrL41kUS+NPQIZK4c8n/FXm99qoWT7a+uD+v9U7g1ozRd2dXwSplc8FG6be9LIzMtwDW9D62b+xbvmTZ8h1HqMnHDBrKLtPOlpIoSEt2luTQXXEBA2O+AqqHYL5CQGo0+wRPFlhc+dSDtHnspUNvfx9QTNard88ROoWjhhEXZVvNuXuvP3vYMidEMJkhID2c35Z1aVuKoHiw375i8xP6LfFvkBQBiiN9zG7ZxVnmDAplzs0j3SYXl3eYLdpQWflcVVA/8YJZu0qY89B80bF+wkTWzv067aUnDgCfWHzrIrtl6YpzODCX/KvKuprMkWn2uxtNil1tajYtSvoGpnWXPnfdYP7m6ZsLN2Ipljhn8OTdGqoupbO9NdF4wdDbfnHEGnwMoaJsSzvZolt5GUlHsQpIG6A7R2WayCYR/L3K4Eu3/4FowONIIfYJ8P8berzNLZIzkgtSDPFAhXH/9EH7lwB5wbEsXJ/mjAmT8W/KcOVei550C1vUHeojM07ydQYLMCyT8+rXd1yYmVVa7uS1A8ddsrjLMcfWfU9yFChKlhX8/P3jnyqMV+bX7H4/AAI+vPjWRU0tS1cYwF+Gds/glpN9K+zqc/oACDn+ydB3fY+nZ2pTsAmAD3VfOAjQkti+g+y0zmbgmF4G8J1CQ21IiQw5QGHaYWWhQvdE77eWVxp1401LfHKTQlSDlvgOPr1uFU/OnbJiyd/uirUsXTEWOB1wOBBTcVwz4pZyp09pawxlD6v78gmc+V0Cei5Ih5mDDo7Vnt40s7K3M7cAj5O2prXu+vwXBuavKHfyAW538zJdjgH21e81hRDkqoLLN395vyAD1y++ddGDQ8+vBMqGnv+2sq5m8J1s6DHGzuRgdpamKlPmi2iR76wuXg0C5sWm3hdyArOllNb6vqdmDO1/kxtjMTJc8aOPRyIiaymPH0xjKxp3T9R+2FKki483DgBwX4XO1Q/dSXtpKXYm+duht35k6PEJd6nYLCMuyh1nzjajmsjEVBAIhObBSfQghEJaJnDmrrhiS2hiAcAUsfm63zWU/00gLhl6+z9Guj0uLkeAfZayAMg1rAEAiXTIpnv8cNi++6oW2cD/4vJW2EVbezIjTPLsHOqLN5LUkgDbvtfyaQ0gava9lLAilUAP7ngzokQ02wGY2ZXNF+/2q8aE/hRT00EcYJPdxMwd29hbnOcIWN6ydIUA9kVf//XItProY8RFGSBoE90cVgFooYeUN7vwuE9tuH1deNxv0sJHoewaXC/m3QZcBnjJppKsfzva4+LyDrODrMhqANMNz8PAQoEYvfjWRd8dCuyiZemK0cD0offcXVlX03JEWnuMsGTZclvibO4ws914cnQmwM5f7l56iyH1i6SUzsruB3KHdv/NkmXL3ToII0hKIwkwev/SQZIvNGYF+tlijcsfvJ2BcC59hvL0kmXLI8BJwDggDtz7zrf46ORtEWW/JXsfK83Wua6kEL8SxNH7aDjjtxNf4tRRABKlruPM2RK4auhtd7hpIC7HAkOBWvsjehUhZi++ddFzi29d9Gr33OeHPXfnkkeGDR392fU7Ptp9mb28/uaWCanRvwQYNHvuHch0ziC7EtSvjmAbj0ksYQ0CBLQg4VgPeRmVOZFsJs7WVAPTdm+nYfw41FT8N0Nv2Wcl311ZVxM/Ak0+KnlbRFmTND9ZopFS2F9Htn3K7TRqo+c3izEIaWd6ReFtLUtX5AHnDu2y7O1oi4vLEWL4yk6TW5auCA5/sWXpCoUDUafrKutqNr5jLTu2Wb8ntpWUHU8pKKqKuhCwpZS/fKz1T6OG9rl1ybLlXUeykcciilQ7TCuBEArfePRpPrKzE68DOzxx3r/sJuJ+P+25gYSAB1qWrlDJxlOAO41wEG+LKAtJfVwX/HackQFesPXYN2Kla36xnPf0AEih/rPjzNn9wEX8//buLzaqMo3j+Hc6bYEiWAUXMEf8U1TUda2wuvjnkDSssig3aoxEb9asuIkbLjaVdTCSNMvFHjFqomLUGLVisgzq7hobEcOu4phAlL8Lboz0D10GW7uosG3plLZz9uJ9T1pYoBfOmTkz8/skzdvT90z6XMB5et7znucxVVz2qz2dlJjg33Mf5tnyTafMPwgES6kr8hVUGdiWZYQt36wf8v3sc5jKaHM3Hly718e/AehFz+5DMZkJh3pOmBKbtwzPYdkhc5c869Pm45MGB/nXNVcTH+jbYKsNLgCmA0fRDviThJKU+6pi2wC2T6+cCNx+8Zolax+IvfunHbFfTLWnvGTHYIPXe2HEIVJAwZ3voB0bTpkP6o93O56rkrK580+gt3/42JSNB596zfHclcmOJzuB1Xb+j43Jlu4CxleypvgTD3wddOqqnUMVcUZ6u3qyXXtqfqitpfPCmVT2/7fJnh5c+zc5njtUgHAjK5SkfHhSbAfAv2sqGKjgcvvj5UA18EV3Q/32dCJVBSy2cy1hxCFSQJ/Z8Xw7Lgom0onUrZgNLqB3M3OqMdkyDATtXu+w42PAJcC3wIsFCKssTPen7m87J0P/4e0AjGSODg9sX/cTgF3z51GROb67MdlyyJ5+px117T9FzouHAAxXxDriWd/PVMZi+2rjC92P93zJaLP2oGLXLZjlu/8AZV2AXEqP47npdCLViWnY7gM3phOpizG1sccunyop596fMXtVVj1939LZjF57VjYmW46f+WPyY9T6kztqYhPYO/A51/19E9m+nkr8EVrr6jhywQVUH+l6FE566yALfFjQoCMolDvl7ob6odoh/yhA2zkVizC1TYN3A9+2pwV/xW5yPFeNxaUUpezYacffYIol3GyPdzqe25b3qEpfEugBpjCakJ8F3ipYROWha2a2lq+umkvrVLJ91RW0XziT3fOuJ3Zi8PDjL7z8D3tecO3f5nju94UKNqpCuVMGmJHxd303gUW7zo8H76IBPN/dUJ+xO++CV6G0fCGlaivmtY/gufLqU+a1lBqCxmTLwNP3Lf0VpkdyJfBMY7Kl7Bsd5EH3rOx5tFf1sHPBgtaatv2z++dcO9GPxyEWaxpzXpCUPyhAjJEXWlI+74T/NrDo4xlVM4AZwHfAOju9GJgNfA+8H1YMIgW21Y6XAu8C94yZ+wHYkPeIykRjsmU3/7+5TkLkeG7vsVWb+4HJ2arqK/quvP5LYrFrgFYqKpoBbJvGYH+FkvJphLJ8DXCsKtbs9NsGm76fBR7qbqgPGon/3o5vOp6bCSsGkQJrxTxDrsYk4LsxTRAAXnU8V883paSc69d0XDQyzRyYhOwDDzc1NQU7rBcCNUAXsLcQMUZdaEl5y13zMy/sPP7K8tZBntqT2drdUP83gHQidTvwS0wZwrJvaC2ly1aoC4ri3A8cZHSDy7ozfEykmLX/fLiOSX71IUzp5GVNTU1j30Meu5dIFRxPI7TlawBnwH/tt20nHgEW2IpGPvCGnX7J8dyDYf5+kQhYD6wE7rJfAEnHczvP/BGRotU+zZ/CA4Nu0vHclWMnbAOK4FWoTfkPrTiEdqds7QLagEmYpbsEpk1dB+ZCJVLSHM/dx8nF9nuBPxQmGpHQtdvxstPM/RTTx3oQ2Jy3iIpMqEnZLk+8bg+bgSfs96scz1WHFikXjwB/xXRBu03doKSEBUm57jRzwUbHzY7n9uYpnqIT6vK19Qzwa2COPV7reK6aT0jZcDy3C7NSJFLqDtjxinQiFXc8d2TMXPB/4C95jqmohL18jb0jvglYAlzueO5j43xERESKUxswgHlkGdyIkU6k6oBrMRt89RrsWeTjThnHc4+gcmoiIiXN8dyRdCK1D7gR+BlmBzbAvXb8RFW8zi70O2URESkrQYe068b8bJkdVTBnHErKIiKSSzvsuBAgnUjNwyToIfQ8eVxKyiIikksf2fHmdCJ1LrDCHr+jpevxKSmLiEjOOJ7bgXmWHAfWYDqjwWjbXjkLJWUREcm1ZjuuwOSZDY7nbitgPEVDSVlERHLteeAb+/1XwO8KGEtRifm+aoKLiEhupROpacBcYIfjuYPjnS+GkrKIiEhEaPlaREQkIpSURUREIkJJWUREJCKUlEVERCJCSVlERCQilJRFREQiQklZREQkIpSURUREIkJJWUREJCKUlEVERCJCSVlERCQilJRFREQiQklZREQkIpSURUREIkJJWUREJCKUlEVERCJCSVlERCQi/gfLLHkDm8acfgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 600x400 with 15 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig=plt.figure(dpi=100)\n",
    "plot_voltage_traces(mem_rec, spk_rec)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 600x400 with 15 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig=plt.figure(dpi=100)\n",
    "plot_voltage_traces(output)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1050x450 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Let's plot the hiddden layer spiking activity for some input stimuli\n",
    "\n",
    "nb_plt = 4\n",
    "gs = GridSpec(1,nb_plt)\n",
    "fig= plt.figure(figsize=(7,3),dpi=150)\n",
    "for i in range(nb_plt):\n",
    "    plt.subplot(gs[i])\n",
    "    plt.imshow(spk_rec[i].detach().cpu().numpy().T,cmap=plt.cm.gray_r, origin=\"lower\" )\n",
    "    if i==0:\n",
    "        plt.xlabel(\"Time\")\n",
    "        plt.ylabel(\"Units\")\n",
    "\n",
    "    sns.despine()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In conclusion, we see that already this simple spiking network solves the classification problem with ~85% accuracy, and there is plenty of room left for tweaking. However, the hidden layer activities do not look very biological. Although the network displays population sparseness in that only a subset of neurons are active at any given time, the individual neurons' firing rates are pathologically high. This pathology is not too surprising since we have not incentivized low activity levels in any way. We will create such an incentive to address this issue by activity regularization in one of the next tutorials."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a rel=\"license\" href=\"http://creativecommons.org/licenses/by/4.0/\"><img alt=\"Creative Commons License\" style=\"border-width:0\" src=\"https://i.creativecommons.org/l/by/4.0/88x31.png\" /></a><br />This work is licensed under a <a rel=\"license\" href=\"http://creativecommons.org/licenses/by/4.0/\">Creative Commons Attribution 4.0 International License</a>."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
